Celltype - consensus time group specific markers in WT ground tissue

Working directory must contain subdirectories,supp_data

supp_data/ should contain the file:

Ground_Tissue_Atlas.rds (get it by running through notebook 8, 9 & 10)
features.tsv.gz

rm(list=ls())
# Set the working directory to where folders named after the samples are located. 
# The folder contains spliced.mtx, unspliced.mtx, barcodes and gene id files, and json files produced by scKB that documents the sequencing stats. 
setwd("/scratch/AG_Ohler/CheWei/scKB")

library(tidyverse)
library(Seurat)
library(RColorBrewer)
library(future)
library(cowplot)
library(ComplexHeatmap)
library(circlize)
library(ggrepel)
#for 200gb ram 
options(future.globals.maxSize = 200000 * 1024^2)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

✔ ggplot2 3.3.2     ✔ purrr   0.3.4
✔ tibble  3.0.4     ✔ dplyr   1.0.2
✔ tidyr   1.1.2     ✔ stringr 1.4.0
✔ readr   1.4.0     ✔ forcats 0.5.0

── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Loading required package: grid

========================================
ComplexHeatmap version 2.6.2
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================


========================================
circlize version 0.4.12
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

rc.integrated <- readRDS("./supp_data/Ground_Tissue_Atlas.rds")

table(rc.integrated$celltype.anno)

           Quiescent Center                   Columella 
                        158                           0 
           Lateral Root Cap                Atrichoblast 
                          0                           0 
                Trichoblast                      Cortex 
                          0                       11073 
                 Endodermis Metaphloem & Companion Cell 
                      11369                           0 
                Protophloem                  Procambium 
                          0                           0 
       Xylem Pole Pericycle       Phloem Pole Pericycle 
                          0                           0 
                 Protoxylem                   Metaxylem 
                          0                           0 

# Plot celltype annotation
order <- c("Quiescent Center", "Ground Tissue","Columella", "Lateral Root Cap", "Atrichoblast", "Trichoblast", "Cortex", "Endodermis", "Metaphloem & Companion Cell","Protophloem", "Xylem", "Procambium","Xylem Pole Pericycle","Phloem Pole Pericycle", "Protoxylem", "Metaxylem", "Unknown")
palette <- c("#9400d3", "#DCD0FF","#5ab953", "#bfef45", "#008080", "#21B6A8", "#82b6ff", "#0000FF","#e6194b", "#dd77ec", "#9a6324", "#ffe119", "#ff9900", "#ffd4e3", "#9a6324", "#ddaa6f", "#EEEEEE")
rc.integrated$celltype.anno <- factor(rc.integrated$celltype.anno, levels = order[sort(match(unique(rc.integrated$celltype.anno),order))]) 
color <- palette[sort(match(unique(rc.integrated$celltype.anno),order))]
options(repr.plot.width=10, repr.plot.height=8)
DimPlot(rc.integrated, reduction = "umap", group.by = "celltype.anno", cols = color)+ggtitle("celltype.anno")

# Plot time
options(repr.plot.width=10, repr.plot.height=8)
DimPlot(rc.integrated, reduction = "umap", group.by = "time.anno",order = c("Distal Columella","Proximal Columella","Distal Lateral Root Cap","Proximal Lateral Root Cap","Maturation","Elongation","Meristem"),cols = c("#e5f5e0", "#a1d99b", "#31a354", "#deebf7", "#3182bd", '#fee0d2','#de2d26'))

table(rc.integrated$consensus.time.group)

  T0   T1   T2   T3   T4   T5   T6   T7   T8   T9 
1278  808 1815 2840 2718 2574 2351 2041 3023 3152 

options(repr.plot.width=5, repr.plot.height=4)
DimPlot(rc.integrated, reduction = "umap", group.by = "consensus.time.group", cols=brewer.pal(10,"Spectral"))

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

(SCR <- FeaturePlot(rc.integrated, features = "AT3G54220",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("SCR"))

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT1G17285",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("AT1G17285")

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT4G09760",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("AT4G09760")

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT5G57620",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("MYB36")

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT2G36100",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("CASP1")

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT1G09750",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("CORTEX")

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT5G07990",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("CYP75B1")

options(repr.plot.width=9, repr.plot.height=8)
DefaultAssay(rc.integrated) <- "integrated"

FeaturePlot(rc.integrated, features = "AT3G21670",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("NPF6.4")

# FindMarkers for celltype and time combination

rc.integrated$cell_group <- paste(rc.integrated$celltype.anno, rc.integrated$consensus.time.group, sep="_")

table(rc.integrated$cell_group)

          Cortex_T0           Cortex_T1           Cortex_T2           Cortex_T3 
                591                 329                 493                1216 
          Cortex_T4           Cortex_T5           Cortex_T6           Cortex_T7 
               1315                1442                1446                1282 
          Cortex_T8           Cortex_T9       Endodermis_T0       Endodermis_T1 
               1757                1202                 610                 398 
      Endodermis_T2       Endodermis_T3       Endodermis_T4       Endodermis_T5 
               1322                1624                1403                1132 
      Endodermis_T6       Endodermis_T7       Endodermis_T8       Endodermis_T9 
                905                 759                1266                1950 
Quiescent Center_T0 Quiescent Center_T1 
                 77                  81 

table(rc.integrated$celltype.anno, rc.integrated$consensus.time.group)

                  
                     T0   T1   T2   T3   T4   T5   T6   T7   T8   T9
  Quiescent Center   77   81    0    0    0    0    0    0    0    0
  Cortex            591  329  493 1216 1315 1442 1446 1282 1757 1202
  Endodermis        610  398 1322 1624 1403 1132  905  759 1266 1950

Idents(rc.integrated) <- "cell_group"

Clust_Markers <- FindAllMarkers(rc.integrated,
                                max.cells.per.ident = 5000,
                                only.pos=T, 
                               test.use="roc")

Calculating cluster Cortex_T4

Calculating cluster Cortex_T6

Calculating cluster Endodermis_T5

Calculating cluster Endodermis_T6

Calculating cluster Cortex_T9

Calculating cluster Endodermis_T1

Calculating cluster Cortex_T5

Calculating cluster Endodermis_T3

Calculating cluster Endodermis_T8

Calculating cluster Cortex_T7

Calculating cluster Endodermis_T7

Calculating cluster Endodermis_T4

Calculating cluster Cortex_T8

Calculating cluster Endodermis_T2

Calculating cluster Cortex_T0

Calculating cluster Endodermis_T9

Calculating cluster Cortex_T3

Calculating cluster Cortex_T1

Calculating cluster Quiescent Center_T1

Calculating cluster Quiescent Center_T0

Calculating cluster Endodermis_T0

Calculating cluster Cortex_T2

feature_names <- read_tsv("./supp_data/features.tsv.gz", col_names = c("gene", "Name", "Type")) %>%
  select(-Type) %>%
  distinct()

── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  gene = col_character(),
  Name = col_character(),
  Type = col_character()
)

feature_names$Name[which(feature_names$gene == "AT1G09750")]="CORTEX"

Clust_Markers <- left_join(Clust_Markers, feature_names)

Joining, by = c("gene", "Name")

Clust_Markers %>% group_by(cluster) %>% tally()

A tibble: 22 × 2

cluster	n
<fct>	<int>
Cortex_T4	519
Cortex_T6	210
Endodermis_T5	271
Endodermis_T6	201
Cortex_T9	464
Endodermis_T1	1803
Cortex_T5	435
Endodermis_T3	483
Endodermis_T8	769
Cortex_T7	198
Endodermis_T7	267
Endodermis_T4	325
Cortex_T8	405
Endodermis_T2	777
Cortex_T0	2771
Endodermis_T9	836
Cortex_T3	608
Cortex_T1	1935
Quiescent Center_T1	1785
Quiescent Center_T0	2436
Endodermis_T0	2514
Cortex_T2	588

Clust_Markers %>% group_by(cluster) %>% top_n(1, myAUC)

A grouped_df: 27 × 17

myAUC	avg_diff	power	pct.1	pct.2	cluster	celltype	stage	gene	Name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank	long_cor	abs_long_cor
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<chr>	<fct>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
0.851	1.6098124	0.702	0.960	0.527	Endodermis_T7	Endodermis	T7	AT3G06125	AT3G06125	0.433	59	66	1	17	1.00000000	1.00000000
0.873	1.3599619	0.746	0.992	0.497	Endodermis_T6	Endodermis	T6	AT1G02900	RALF1	0.495	30	63	1	10	0.98787879	0.98787879
0.881	2.6551783	0.762	0.878	0.227	Cortex_T2	Cortex	T2	AT5G14020	AT5G14020	0.651	1	9	1	1	-0.97575758	0.97575758
0.933	2.2197096	0.866	0.976	0.181	Cortex_T8	Cortex	T8	AT5G15180	PER56	0.795	1	36	1	1	0.95151515	0.95151515
0.959	2.5184213	0.918	0.996	0.201	Cortex_T9	Cortex	T9	AT5G15180	PER56	0.795	2	95	1	13	0.95151515	0.95151515
0.851	1.5120091	0.702	0.916	0.240	Endodermis_T7	Endodermis	T7	AT5G66390	PER72	0.676	1	75	2	6	0.92727273	0.92727273
0.866	1.5772820	0.732	0.934	0.337	Cortex_T7	Cortex	T7	AT1G13930	AT1G13930	0.597	3	35	1	2	0.87878788	0.87878788
0.979	3.6306420	0.958	1.000	0.132	Endodermis_T0	Endodermis	T0	AT5G02450	RPL36C	0.868	140	28	1	6	-0.87878788	0.87878788
0.952	2.7290143	0.904	0.977	0.119	Endodermis_T1	Endodermis	T1	AT5G02960	RPS23B	0.858	4	448	3	107	-0.87878788	0.87878788
0.979	3.3209090	0.958	1.000	0.111	Endodermis_T0	Endodermis	T0	AT4G15000	RPL27C	0.889	55	95	2	3	-0.79393939	0.79393939
0.970	2.8440779	0.940	1.000	0.096	Cortex_T0	Cortex	T0	AT1G27400	RPL17A	0.904	12	175	1	4	-0.78181818	0.78181818
0.952	3.1418255	0.904	0.962	0.106	Endodermis_T1	Endodermis	T1	AT4G18730	RPL11C	0.856	7	228	2	25	-0.78181818	0.78181818
0.952	3.2032964	0.904	0.965	0.109	Endodermis_T1	Endodermis	T1	AT2G19730	RPL28A	0.856	6	202	1	20	-0.76969697	0.76969697
0.952	3.1430591	0.904	0.988	0.125	Cortex_T1	Cortex	T1	AT1G57660	RPL21E	0.863	21	57	1	1	-0.70909091	0.70909091
0.972	3.4287747	0.944	0.983	0.122	Endodermis_T9	Endodermis	T9	AT2G39430	DIR9	0.861	1	71	1	4	0.70909091	0.70909091
0.915	2.1388130	0.830	0.943	0.219	Endodermis_T3	Endodermis	T3	AT1G62480	AT1G62480	0.724	1	7	1	1	-0.61212121	0.61212121
0.915	2.3013423	0.830	0.983	0.382	Endodermis_T2	Endodermis	T2	AT1G11580	ATPMEPCRA	0.601	31	41	1	7	-0.61212121	0.61212121
0.867	1.6638362	0.734	0.954	0.345	Cortex_T6	Cortex	T6	AT2G39010	PIP2-6	0.609	2	10	1	1	0.58787879	0.58787879
0.901	1.9858252	0.802	0.959	0.287	Endodermis_T4	Endodermis	T4	AT5G13910	LEP	0.672	2	4	1	1	-0.35757576	0.35757576
0.990	4.0380846	0.980	1.000	0.073	Quiescent Center_T0	Quiescent Center	T0	AT2G28900	OEP161	0.927	5	40	1	2	-0.32121212	0.32121212
0.927	0.3517676	0.854	0.844	0.115	Endodermis_T8	Endodermis	T8	AT1G55610	BRL1	0.729	4	711	1	169	0.13939394	0.13939394
0.886	1.8773184	0.772	0.955	0.295	Cortex_T4	Cortex	T4	AT5G11420	AT5G11420	0.660	2	25	1	1	0.09090909	0.09090909
0.884	1.8559454	0.768	0.945	0.292	Cortex_T5	Cortex	T5	AT5G11420	AT5G11420	0.653	2	21	1	3	0.09090909	0.09090909
0.995	5.6111516	0.990	0.975	0.025	Quiescent Center_T1	Quiescent Center	T1	AT4G27520	ENODL2	0.950	2	10	1	2	-0.07878788	0.07878788
0.891	1.8751420	0.782	0.981	0.371	Endodermis_T5	Endodermis	T5	AT4G16447	AT4G16447	0.610	6	16	1	2	-0.06666667	0.06666667
0.990	3.9083440	0.980	1.000	0.050	Quiescent Center_T0	Quiescent Center	T0	AT5G10625	FLP2	0.950	1	60	2	4	-0.05454545	0.05454545
0.892	1.7467310	0.784	0.895	0.208	Cortex_T3	Cortex	T3	AT4G15160	AT4G15160	0.687	1	108	1	10	-0.03030303	0.03030303

Clust_Markers <- separate(Clust_Markers, cluster, into=c("celltype", "stage"), sep="_", remove=F)

Clust_Markers <- mutate(Clust_Markers, pct.diff=pct.1-pct.2)

Clust_Markers <- arrange(Clust_Markers, desc(pct.diff)) %>%
group_by(cluster) %>%
mutate(pct.diff_rank=dplyr::row_number()) %>%
arrange(desc(avg_diff)) %>%
mutate(avg_diff_rank=dplyr::row_number()) %>%
arrange(desc(myAUC)) %>%
mutate(myAUC_rank=dplyr::row_number()) %>%
mutate(combined_rank_raw=(pct.diff_rank + avg_diff_rank + myAUC_rank)/3) %>%
arrange(combined_rank_raw) %>%
mutate(combined_rank=dplyr::row_number()) %>%
select(-combined_rank_raw) %>%
arrange(combined_rank)
Clust_Markers

A grouped_df: 20600 × 17

myAUC	avg_diff	power	pct.1	pct.2	cluster	celltype	stage	gene	Name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank	long_cor	abs_long_cor
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<chr>	<chr>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
0.901	1.985825	0.802	0.959	0.287	Endodermis_T4	Endodermis	T4	AT5G13910	LEP	0.672	2	4	1	1	-0.357575758	0.357575758
0.915	2.138813	0.830	0.943	0.219	Endodermis_T3	Endodermis	T3	AT1G62480	AT1G62480	0.724	1	7	1	1	-0.612121212	0.612121212
0.881	2.655178	0.762	0.878	0.227	Cortex_T2	Cortex	T2	AT5G14020	AT5G14020	0.651	1	9	1	1	-0.975757576	0.975757576
0.993	5.784636	0.986	0.988	0.045	Quiescent Center_T1	Quiescent Center	T1	AT2G41800	AT2G41800	0.943	4	6	2	1	-0.200000000	0.200000000
0.880	1.977404	0.760	0.927	0.253	Cortex_T5	Cortex	T5	AT1G41830	SKS6	0.674	1	9	2	1	-0.175757576	0.175757576
0.867	1.663836	0.734	0.954	0.345	Cortex_T6	Cortex	T6	AT2G39010	PIP2-6	0.609	2	10	1	1	0.587878788	0.587878788
0.856	1.985059	0.712	0.940	0.330	Endodermis_T5	Endodermis	T5	AT2G16700	ADF5	0.610	7	8	7	1	-0.006060606	0.006060606
0.935	3.109681	0.870	0.953	0.189	Cortex_T9	Cortex	T9	AT5G44480	DUR	0.764	4	17	4	1	0.915151515	0.915151515
0.876	2.197686	0.752	0.910	0.311	Cortex_T3	Cortex	T3	AT5G62210	AT5G62210	0.599	7	15	3	1	-0.709090909	0.709090909
0.886	1.877318	0.772	0.955	0.295	Cortex_T4	Cortex	T4	AT5G11420	AT5G11420	0.660	2	25	1	1	0.090909091	0.090909091
0.866	1.792225	0.732	0.965	0.378	Endodermis_T6	Endodermis	T6	AT4G16447	AT4G16447	0.587	4	24	2	1	-0.066666667	0.066666667
0.904	2.565345	0.808	0.915	0.171	Endodermis_T2	Endodermis	T2	AT3G23730	XTH16	0.744	1	28	3	1	-0.806060606	0.806060606
0.829	1.921177	0.658	0.761	0.217	Cortex_T7	Cortex	T7	AT3G18200	AT3G18200	0.544	14	12	10	1	0.903030303	0.903030303
0.984	4.137071	0.968	0.987	0.055	Quiescent Center_T0	Quiescent Center	T0	AT3G60900	FLA10	0.932	3	29	6	1	-0.248484848	0.248484848
0.933	2.219710	0.866	0.976	0.181	Cortex_T8	Cortex	T8	AT5G15180	PER56	0.795	1	36	1	1	0.951515152	0.951515152
0.840	1.908193	0.680	0.900	0.337	Endodermis_T7	Endodermis	T7	AT2G15340	AT2G15340	0.563	7	37	4	1	0.903030303	0.903030303
0.843	2.688312	0.686	0.708	0.060	Endodermis_T8	Endodermis	T8	AT1G13600	AtbZIP58	0.648	14	15	25	1	0.648484848	0.648484848
0.967	5.279871	0.934	0.964	0.280	Endodermis_T9	Endodermis	T9	AT1G02500	SAM1	0.684	55	2	2	1	0.321212121	0.321212121
0.950	3.611536	0.900	0.960	0.122	Endodermis_T1	Endodermis	T1	AT1G57660	RPL21E	0.838	26	39	5	1	-0.709090909	0.709090909
0.952	3.143059	0.904	0.988	0.125	Cortex_T1	Cortex	T1	AT1G57660	RPL21E	0.863	24	57	1	1	-0.709090909	0.709090909
0.976	3.490443	0.952	1.000	0.105	Endodermis_T0	Endodermis	T0	AT4G18100	RPL32A	0.895	36	46	8	1	-0.660606061	0.660606061
0.969	2.904778	0.938	0.986	0.078	Cortex_T0	Cortex	T0	AT2G20490	NOP10	0.908	8	149	2	1	-0.248484848	0.248484848
0.886	1.867908	0.772	0.958	0.272	Endodermis_T4	Endodermis	T4	AT2G23560	MES7	0.686	1	9	2	2	0.078787879	0.078787879
0.866	2.037492	0.732	0.924	0.293	Cortex_T5	Cortex	T5	AT4G34260	FUC95A	0.631	3	3	6	2	0.272727273	0.272727273
0.995	5.611152	0.990	0.975	0.025	Quiescent Center_T1	Quiescent Center	T1	AT4G27520	ENODL2	0.950	2	10	1	2	-0.078787879	0.078787879
0.910	2.130515	0.820	0.972	0.279	Endodermis_T3	Endodermis	T3	AT5G13910	LEP	0.693	3	8	3	2	-0.357575758	0.357575758
0.891	1.875142	0.782	0.981	0.371	Endodermis_T5	Endodermis	T5	AT4G16447	AT4G16447	0.610	6	16	1	2	-0.066666667	0.066666667
0.847	2.965077	0.694	0.700	0.165	Cortex_T2	Cortex	T2	AT2G45050	GATA2	0.535	21	1	5	2	-0.866666667	0.866666667
0.919	3.414707	0.838	0.939	0.252	Cortex_T9	Cortex	T9	AT4G34180	CYCLASE1	0.687	13	3	12	2	0.915151515	0.915151515
0.872	1.858140	0.744	0.901	0.249	Cortex_T4	Cortex	T4	AT5G12940	AT5G12940	0.652	3	28	3	2	-0.466666667	0.466666667
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
0.291	0.9024948	0.418	0.090	0.381	Cortex_T0	Cortex	T0	AT2G07678	AT2G07678	-0.291	2766	2370	2763	2742	0.91515152	0.91515152
0.707	0.2615472	0.414	0.592	0.199	Cortex_T0	Cortex	T0	AT4G14840	AT4G14840	0.393	2475	2766	2659	2743	-0.60000000	0.60000000
0.705	0.6576205	0.410	0.530	0.199	Cortex_T0	Cortex	T0	AT3G04130	AT3G04130	0.331	2678	2532	2692	2744	-0.52727273	0.52727273
0.707	0.4129214	0.414	0.538	0.164	Cortex_T0	Cortex	T0	AT2G07644	AT2G07644	0.374	2555	2693	2656	2745	-0.27272727	0.27272727
0.704	0.4836549	0.408	0.572	0.194	Cortex_T0	Cortex	T0	AT2G31890	RAP	0.378	2534	2654	2719	2746	0.21212121	0.21212121
0.717	0.3329135	0.434	0.687	0.355	Cortex_T0	Cortex	T0	AT3G16640	TCTP1	0.332	2674	2732	2504	2747	-0.93939394	0.93939394
0.719	0.3841605	0.438	0.139	0.039	Cortex_T0	Cortex	T0	AT1G69850	NPF4.6	0.100	2758	2704	2465	2748	0.27272727	0.27272727
0.701	0.7826382	0.402	0.474	0.176	Cortex_T0	Cortex	T0	AT4G37590	NPY5	0.298	2718	2459	2758	2749	-0.12727273	0.12727273
0.713	0.4579377	0.426	0.673	0.362	Cortex_T0	Cortex	T0	AT5G48580	FKBP15-2	0.311	2706	2666	2564	2750	-0.93939394	0.93939394
0.717	0.2759030	0.434	0.631	0.304	Cortex_T0	Cortex	T0	AT3G10220	TFCB	0.327	2683	2758	2505	2751	-0.95151515	0.95151515
0.706	0.5790248	0.412	0.582	0.257	Cortex_T0	Cortex	T0	AT3G57550	AGK2	0.325	2689	2587	2671	2752	-0.46666667	0.46666667
0.701	0.4747664	0.402	0.628	0.253	Cortex_T0	Cortex	T0	AT3G04980	AT3G04980	0.375	2547	2658	2761	2753	-0.26060606	0.26060606
0.711	0.4291586	0.422	0.667	0.344	Cortex_T0	Cortex	T0	AT5G51120	PABN1	0.323	2691	2686	2596	2754	-0.93939394	0.93939394
0.713	0.2543547	0.426	0.650	0.303	Cortex_T0	Cortex	T0	AT5G64610	HAM1	0.347	2639	2771	2566	2755	-0.97575758	0.97575758
0.702	0.5002980	0.404	0.636	0.271	Cortex_T0	Cortex	T0	AT3G21350	MED6	0.365	2584	2648	2744	2756	-0.95151515	0.95151515
0.714	0.3878292	0.428	0.486	0.217	Cortex_T0	Cortex	T0	AT4G35905	AT4G35905	0.269	2729	2702	2547	2757	-0.95151515	0.95151515
0.707	0.4371539	0.414	0.604	0.268	Cortex_T0	Cortex	T0	AT1G14510	AL7	0.336	2670	2676	2654	2758	-0.90303030	0.90303030
0.704	0.5037687	0.408	0.553	0.208	Cortex_T0	Cortex	T0	AT1G30480	DRT111	0.345	2643	2644	2718	2759	-0.40606061	0.40606061
0.706	0.3248766	0.412	0.668	0.309	Cortex_T0	Cortex	T0	AT1G09150	AT1G09150	0.359	2604	2734	2674	2760	-0.95151515	0.95151515
0.254	0.7479319	0.492	0.029	0.309	Cortex_T0	Cortex	T0	AT3G10450	SCPL7	-0.280	2765	2480	2767	2761	0.29696970	0.29696970
0.702	0.2572413	0.404	0.487	0.101	Cortex_T0	Cortex	T0	AT3G28150	AXY4L	0.386	2502	2769	2746	2762	-0.43030303	0.43030303
0.708	0.3945001	0.416	0.506	0.178	Cortex_T0	Cortex	T0	AT1G48030	LPD1	0.328	2682	2700	2643	2763	-0.15151515	0.15151515
0.702	0.5557036	0.404	0.694	0.376	Cortex_T0	Cortex	T0	AT5G42190	SKP1B	0.318	2700	2606	2743	2764	-0.91515152	0.91515152
0.704	0.5109657	0.408	0.584	0.282	Cortex_T0	Cortex	T0	AT1G29340	PUB17	0.302	2714	2638	2717	2765	-0.07878788	0.07878788
0.261	0.6517756	0.478	0.081	0.400	Cortex_T0	Cortex	T0	AT1G31170	ATSRX	-0.319	2767	2537	2766	2766	0.80606061	0.80606061
0.702	0.4294830	0.404	0.599	0.313	Cortex_T0	Cortex	T0	AT4G14420	AT4G14420	0.286	2722	2683	2745	2767	-0.93939394	0.93939394
0.288	0.5294175	0.424	0.074	0.259	Cortex_T0	Cortex	T0	AT5G03180	AT5G03180	-0.185	2763	2629	2765	2768	0.84242424	0.84242424
0.111	0.4886949	0.778	0.049	0.566	Cortex_T0	Cortex	T0	AT2G24040	AT2G24040	-0.517	2771	2653	2771	2769	0.24848485	0.24848485
0.704	0.3193795	0.408	0.489	0.237	Cortex_T0	Cortex	T0	AT5G03140	LECRK82	0.252	2739	2738	2720	2770	-0.79393939	0.79393939
0.217	0.4240761	0.566	0.096	0.314	Cortex_T0	Cortex	T0	AT2G45290	TKL-2	-0.218	2764	2687	2769	2771	0.43030303	0.43030303

filter(Clust_Markers, celltype %in% c("Cortex", "Endodermis") & myAUC>=0.75) %>%
group_by(celltype) %>% 
tally()

A tibble: 2 × 2

celltype	n
<chr>	<int>
Cortex	4852
Endodermis	4925

filter(Clust_Markers, celltype %in% c("Cortex", "Endodermis") & myAUC>=0.75) %>%
group_by(cluster) %>% 
tally()

A tibble: 20 × 2

cluster	n
<fct>	<int>
Cortex_T4	253
Cortex_T6	86
Endodermis_T5	140
Endodermis_T6	81
Cortex_T9	270
Endodermis_T1	1205
Cortex_T5	214
Endodermis_T3	253
Endodermis_T8	380
Cortex_T7	77
Endodermis_T7	67
Endodermis_T4	174
Cortex_T8	196
Endodermis_T2	399
Cortex_T0	1961
Endodermis_T9	467
Cortex_T3	275
Cortex_T1	1298
Endodermis_T0	1759
Cortex_T2	222

table(Clust_Markers$stage)

  T0   T1   T2   T3   T4   T5   T6   T7   T8   T9 
7721 5523 1365 1091  844  706  411  465 1174 1300 

Clust_Markers$stage <- factor(Clust_Markers$stage, levels=c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"))

# write markers with AUC 0.75 and above to a csv
# "Cortex", "Endodermis"

Clust_Markers_to_write <- filter(Clust_Markers, celltype %in% c("Cortex", "Endodermis") & myAUC>=0.75) %>% arrange(celltype) %>% arrange(stage) %>% arrange(combined_rank)

write.csv(Clust_Markers_to_write, "./supp_data/WT_Endodermis_Cortex_markers_AUC_0.75.csv", row.names=F)

number_de_genes <- filter(Clust_Markers, celltype %in% c("Cortex", "Endodermis") & myAUC>=0.75) %>%
group_by(cluster) %>% 
tally() %>%
separate(cluster, into=c("celltype", "stage"), sep="_")

number_de_genes$stage <- factor(number_de_genes$stage, levels=c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"))

(number_de_genes <- arrange(number_de_genes, celltype) %>% arrange(stage) %>% rename("n_de_genes"=n))

write.csv(number_de_genes, "./supp_data/WT_Endodermis_Cortex_markers_AUC_0.75_n_DE.csv", row.names=F)

A tibble: 20 × 3

celltype	stage	n_de_genes
<chr>	<fct>	<int>
Cortex	T0	1961
Endodermis	T0	1759
Cortex	T1	1298
Endodermis	T1	1205
Cortex	T2	222
Endodermis	T2	399
Cortex	T3	275
Endodermis	T3	253
Cortex	T4	253
Endodermis	T4	174
Cortex	T5	214
Endodermis	T5	140
Cortex	T6	86
Endodermis	T6	81
Cortex	T7	77
Endodermis	T7	67
Cortex	T8	196
Endodermis	T8	380
Cortex	T9	270
Endodermis	T9	467

table(Clust_Markers$stage)

  T0   T1   T2   T3   T4   T5   T6   T7   T8   T9 
7721 5523 1365 1091  844  706  411  465 1174 1300 

Clust_Markers$stage <- factor(Clust_Markers$stage, levels=c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"))

rc.integrated$consensus.time.group <- factor(rc.integrated$consensus.time.group, levels=c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"))

Idents(rc.integrated) <- "consensus.time.group"

# pseudobulk of each stage

  afm <- as.matrix(rc.integrated@assays$integrated@data)
  pooled <- matrix(nrow=nrow(afm), ncol = 0)

  for (i in unique(rc.integrated@meta.data$consensus.time.group)) {
    m <- afm[,which(rc.integrated@meta.data$consensus.time.group==i)]
    pooled <- cbind(pooled, rowSums(m)/ncol(m))
  }

unique(rc.integrated@meta.data$consensus.time.group)

1. T4
2. T6
3. T5
4. T9
5. T1
6. T3
7. T8
8. T7
9. T2
10. T0

Levels:

1. 'T0'
2. 'T1'
3. 'T2'
4. 'T3'
5. 'T4'
6. 'T5'
7. 'T6'
8. 'T7'
9. 'T8'
10. 'T9'

colnames(pooled) <- unique(rc.integrated@meta.data$consensus.time.group)

pooled_df <- as_tibble(pooled, rownames = "gene")

pooled_df_long <- pivot_longer(pooled_df, cols = 2:ncol(pooled_df), names_to = "stage", values_to = "expression")

long_order <- tibble(stage=paste("T", rep(0:9), sep=""), 
                     order=rep(1:10))

long_order

A tibble: 10 × 2

stage	order
<chr>	<int>
T0	1
T1	2
T2	3
T3	4
T4	5
T5	6
T6	7
T7	8
T8	9
T9	10

(pooled_df_long <- left_join(pooled_df_long, long_order) %>%
arrange(order))

Joining, by = "stage"

A tibble: 175130 × 4

gene	stage	expression	order
<chr>	<chr>	<dbl>	<int>
AT1G05260	T0	-0.9756156	1
AT3G59370	T0	-2.1390137	1
AT2G36100	T0	-0.5732628	1
AT1G12080	T0	-1.9192388	1
AT1G12090	T0	-0.8453402	1
AT4G11290	T0	-1.0448425	1
AT5G42180	T0	-0.6208027	1
AT5G66390	T0	-0.7698246	1
AT2G32300	T0	-1.1660403	1
AT2G02130	T0	-1.4222126	1
AT3G11550	T0	-0.4656793	1
AT4G35350	T0	-0.5410920	1
AT1G30750	T0	-0.3857508	1
AT4G40090	T0	-0.8454381	1
AT1G20850	T0	-0.5737647	1
AT2G39430	T0	-0.4500255	1
AT1G75750	T0	-4.1100915	1
AT4G13580	T0	-0.4837341	1
AT4G25820	T0	-0.7139935	1
AT4G23690	T0	-1.0530672	1
AT1G26820	T0	-0.6452091	1
AT5G64100	T0	-2.2258046	1
AT1G65310	T0	-0.9921584	1
AT2G40113	T0	-0.3543588	1
AT5G17820	T0	-2.2810320	1
AT3G24020	T0	-0.4305779	1
AT5G04200	T0	-0.6187564	1
AT3G54580	T0	-1.0985949	1
AT3G09925	T0	-0.9476313	1
AT5G46900	T0	-2.7217745	1
⋮	⋮	⋮	⋮
AT4G22220	T9	-0.10157676	10
AT2G23090	T9	0.06355433	10
AT2G46490	T9	-0.39573483	10
AT3G27240	T9	0.48059784	10
AT1G07140	T9	0.03226931	10
AT1G76200	T9	0.43662989	10
AT5G08670	T9	0.31597980	10
AT1G04357	T9	-0.02525383	10
AT1G64230	T9	0.10779913	10
AT2G02050	T9	0.28476715	10
AT3G09840	T9	0.40582692	10
AT5G47030	T9	0.11472406	10
AT1G10590	T9	-0.30711638	10
AT1G08480	T9	0.26458326	10
AT5G61330	T9	0.03782800	10
AT4G02580	T9	-0.05323921	10
AT5G08060	T9	0.40793210	10
AT1G75950	T9	-0.19641445	10
AT2G20360	T9	0.35039274	10
AT4G30010	T9	0.31325351	10
AT4G06395	T9	-0.03931359	10
AT3G55440	T9	0.08067285	10
AT3G03100	T9	0.41172866	10
AT5G54760	T9	-0.08012683	10
AT2G33040	T9	0.31443362	10
AT2G42680	T9	0.08661184	10
AT5G11770	T9	0.42159057	10
AT5G08290	T9	-0.02513125	10
AT5G53300	T9	0.08732696	10
AT5G64400	T9	0.22821801	10

(long_corrs <- pooled_df_long %>%
group_by(gene) %>%
summarise(long_cor = cor(order, expression, method = "spearman")))

`summarise()` ungrouping output (override with `.groups` argument)

A tibble: 17513 × 2

gene	long_cor
<chr>	<dbl>
AT1G01010	0.72121212
AT1G01020	-0.33333333
AT1G01030	-0.26060606
AT1G01040	0.73333333
AT1G01050	-0.91515152
AT1G01070	0.20000000
AT1G01080	-0.04242424
AT1G01090	-0.87878788
AT1G01100	-0.91515152
AT1G01110	-0.62424242
AT1G01120	0.98787879
AT1G01130	0.18787879
AT1G01160	-0.50303030
AT1G01170	0.16363636
AT1G01180	0.86666667
AT1G01200	0.46666667
AT1G01210	-0.26060606
AT1G01220	-0.92727273
AT1G01225	-0.56363636
AT1G01230	0.11515152
AT1G01240	0.76969697
AT1G01260	-0.91515152
AT1G01290	-0.63636364
AT1G01300	-0.83030303
AT1G01310	0.84242424
AT1G01320	-0.05454545
AT1G01340	0.96363636
AT1G01350	0.47878788
AT1G01360	-0.56363636
AT1G01370	-0.61212121
⋮	⋮
AT5G67320	0.09090909
AT5G67330	0.90303030
AT5G67340	0.98787879
AT5G67360	-0.51515152
AT5G67370	0.36969697
AT5G67380	0.50303030
AT5G67385	-0.32121212
AT5G67390	-0.64848485
AT5G67400	0.72121212
AT5G67410	-0.81818182
AT5G67420	-0.04242424
AT5G67440	0.33333333
AT5G67450	0.50303030
AT5G67460	-0.17575758
AT5G67470	0.24848485
AT5G67488	0.16363636
AT5G67500	0.78181818
AT5G67510	-0.20000000
AT5G67520	0.83030303
AT5G67530	-0.58787879
AT5G67540	-0.80606061
AT5G67560	0.91515152
AT5G67570	-0.87878788
AT5G67580	0.68484848
AT5G67590	1.00000000
AT5G67600	0.58787879
AT5G67610	-0.27272727
AT5G67620	0.66060606
AT5G67630	-0.12727273
AT5G67640	-0.12727273

Clust_Markers <- left_join(Clust_Markers, long_corrs) %>%
mutate(abs_long_cor=abs(long_cor)) %>%
arrange(-abs_long_cor)
Clust_Markers

Joining, by = c("gene", "long_cor")

A grouped_df: 20600 × 17

myAUC	avg_diff	power	pct.1	pct.2	cluster	celltype	stage	gene	Name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank	long_cor	abs_long_cor
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<chr>	<fct>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
0.851	1.6098124	0.702	0.960	0.527	Endodermis_T7	Endodermis	T7	AT3G06125	AT3G06125	0.433	59	66	1	17	1	1
0.906	2.2766098	0.812	0.985	0.515	Endodermis_T8	Endodermis	T8	AT3G06125	AT3G06125	0.470	158	50	4	27	1	1
0.938	3.6469189	0.876	0.947	0.169	Endodermis_T1	Endodermis	T1	AT2G31610	RPS3A	0.778	124	29	119	36	-1	1
0.838	3.0433052	0.676	0.891	0.401	Cortex_T9	Cortex	T9	AT1G11910	APA1	0.490	117	20	77	45	1	1
0.890	4.6947655	0.780	0.900	0.340	Endodermis_T9	Endodermis	T9	AT5G19590	AT5G19590	0.560	164	8	72	47	1	1
0.932	4.8121159	0.864	0.971	0.507	Endodermis_T9	Endodermis	T9	AT5G55850	NOI	0.464	298	7	30	57	1	1
0.770	1.1769976	0.540	0.850	0.414	Endodermis_T6	Endodermis	T6	AT3G21240	4CL2	0.436	63	95	54	58	1	1
0.982	4.9356199	0.964	1.000	0.285	Quiescent Center_T1	Quiescent Center	T1	AT5G65700	BAM1	0.715	268	39	17	67	-1	1
0.891	4.2146378	0.782	0.927	0.468	Endodermis_T9	Endodermis	T9	AT3G61260	AT3G61260	0.459	303	23	70	72	1	1
0.747	1.1724069	0.494	0.853	0.487	Cortex_T7	Cortex	T7	AT4G39080	VHA-A3	0.366	109	78	80	82	1	1
0.888	2.3030605	0.776	0.945	0.412	Endodermis_T9	Endodermis	T9	AT2G37040	PAL1	0.533	191	172	77	87	1	1
0.849	3.0011386	0.698	0.933	0.407	Endodermis_T9	Endodermis	T9	AT4G22120	CSC1	0.526	203	102	139	89	1	1
0.847	2.5222584	0.694	0.941	0.509	Cortex_T9	Cortex	T9	AT5G43600	UAH	0.432	196	93	60	100	1	1
0.788	2.3322872	0.576	0.856	0.518	Cortex_T8	Cortex	T8	AT1G05850	CTL1	0.338	273	18	105	101	1	1
0.960	3.8584946	0.920	0.975	0.258	Quiescent Center_T1	Quiescent Center	T1	AT1G14720	XTH28	0.717	262	178	58	101	-1	1
0.834	2.5700739	0.668	0.917	0.485	Cortex_T9	Cortex	T9	AT4G39080	VHA-A3	0.432	195	86	85	104	1	1
0.718	1.6598366	0.436	0.821	0.498	Cortex_T7	Cortex	T7	AT5G34850	PAP26	0.323	147	27	143	109	1	1
0.973	2.9487679	0.946	0.992	0.160	Endodermis_T0	Endodermis	T0	AT2G31610	RPS3A	0.832	219	240	43	114	-1	1
0.842	2.3633329	0.684	0.923	0.494	Cortex_T9	Cortex	T9	AT5G34850	PAP26	0.429	198	134	69	119	1	1
0.797	1.6292793	0.594	0.920	0.500	Cortex_T8	Cortex	T8	AT5G43600	UAH	0.420	147	200	89	122	1	1
0.732	1.2048915	0.464	0.817	0.527	Cortex_T7	Cortex	T7	AT1G05850	CTL1	0.290	170	75	106	125	1	1
0.798	0.7774996	0.596	0.734	0.247	Cortex_T2	Cortex	T2	AT5G02500	MED37E	0.487	55	416	46	125	-1	1
0.828	2.0517490	0.656	0.945	0.472	Cortex_T9	Cortex	T9	AT3G11780	AT3G11780	0.473	139	200	97	128	1	1
0.866	1.2079462	0.732	0.830	0.174	Endodermis_T9	Endodermis	T9	AT5G42460	AT5G42460	0.656	71	388	114	129	1	1
0.793	3.3132389	0.586	0.784	0.281	Endodermis_T9	Endodermis	T9	AT1G45160	AT1G45160	0.503	236	76	270	132	1	1
0.737	0.9381952	0.474	0.893	0.615	Endodermis_T6	Endodermis	T6	AT1G44170	ALDH3H1	0.278	171	137	101	149	1	1
0.800	1.3280052	0.600	0.894	0.485	Cortex_T8	Cortex	T8	AT5G34850	PAP26	0.409	162	275	85	149	1	1
0.949	3.0583001	0.898	0.790	0.061	Quiescent Center_T1	Quiescent Center	T1	AT5G23820	ML3	0.729	230	416	84	151	-1	1
0.946	2.4256048	0.892	0.985	0.171	Cortex_T1	Cortex	T1	AT2G31610	RPS3A	0.814	121	514	55	152	-1	1
0.715	0.8701974	0.430	0.847	0.514	Cortex_T7	Cortex	T7	AT5G43600	UAH	0.333	141	123	151	154	1	1
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
0.779	2.1044479	0.558	0.341	0.039	Endodermis_T0	Endodermis	T0	AT2G40200	BHLH51	0.302	2387	983	1378	1615	-0.006060606	0.006060606
0.719	1.6471551	0.438	0.701	0.047	Quiescent Center_T0	Quiescent Center	T0	AT2G18500	OFP7	0.654	968	1371	2196	1647	0.006060606	0.006060606
0.798	1.7031908	0.596	0.506	0.034	Cortex_T0	Cortex	T0	AT2G40200	BHLH51	0.472	2016	1504	1260	1667	-0.006060606	0.006060606
0.814	0.7249876	0.628	0.766	0.178	Quiescent Center_T0	Quiescent Center	T0	AT1G30520	AAE14	0.588	1345	2033	1253	1687	0.006060606	0.006060606
0.721	0.7262378	0.442	0.544	0.108	Cortex_T1	Cortex	T1	AT5G45740	AT5G45740	0.436	1380	1692	1650	1727	-0.006060606	0.006060606
0.702	0.5558862	0.404	0.617	0.245	Quiescent Center_T1	Quiescent Center	T1	AT1G51560	AT1G51560	0.372	1542	1607	1708	1740	0.006060606	0.006060606
0.710	3.0171315	0.420	0.241	0.024	Endodermis_T0	Endodermis	T0	AT5G56460	PBL16	0.217	2463	197	2331	1745	0.006060606	0.006060606
0.774	1.7804675	0.548	0.740	0.234	Quiescent Center_T0	Quiescent Center	T0	AT4G04350	EMB2369	0.506	1842	1283	1654	1757	-0.006060606	0.006060606
0.704	3.4770447	0.408	0.416	0.039	Quiescent Center_T0	Quiescent Center	T0	AT5G52870	MAKR5	0.377	2260	182	2338	1758	0.006060606	0.006060606
0.773	1.7735576	0.546	0.633	0.174	Cortex_T0	Cortex	T0	AT3G62310	AT3G62310	0.459	2112	1404	1596	1796	-0.006060606	0.006060606
0.710	1.3150559	0.420	0.666	0.383	Cortex_T1	Cortex	T1	AT1G70310	SPDSYN2	0.283	1896	1326	1790	1812	-0.006060606	0.006060606
0.733	1.9598097	0.466	0.587	0.094	Cortex_T0	Cortex	T0	AT2G43445	AT2G43445	0.493	1844	1150	2208	1850	0.006060606	0.006060606
0.737	2.1951330	0.474	0.248	0.011	Endodermis_T0	Endodermis	T0	AT1G21140	AT1G21140	0.237	2446	862	1941	1854	-0.006060606	0.006060606
0.752	1.3819277	0.504	0.552	0.128	Endodermis_T0	Endodermis	T0	AT3G14580	AT3G14580	0.424	1962	1700	1730	1910	-0.006060606	0.006060606
0.736	2.1224751	0.472	0.479	0.067	Cortex_T0	Cortex	T0	AT1G64110	AT1G64110	0.412	2378	921	2166	1949	0.006060606	0.006060606
0.867	0.3160424	0.734	0.247	0.020	Quiescent Center_T0	Quiescent Center	T0	AT3G50740	UGT72E1	0.227	2358	2381	790	2061	0.006060606	0.006060606
0.714	2.1115780	0.428	0.519	0.080	Cortex_T0	Cortex	T0	AT5G05740	EGY2	0.439	2240	936	2534	2062	0.006060606	0.006060606
0.780	0.3488409	0.560	0.485	0.048	Endodermis_T0	Endodermis	T0	AT4G33170	PCMP-H53	0.437	1885	2476	1375	2065	0.006060606	0.006060606
0.748	1.2753085	0.496	0.597	0.091	Cortex_T0	Cortex	T0	AT4G37380	ELI1	0.506	1745	2019	1983	2079	0.006060606	0.006060606
0.731	1.7699078	0.462	0.236	0.068	Endodermis_T0	Endodermis	T0	AT5G25170	AT5G25170	0.168	2476	1318	2034	2107	0.006060606	0.006060606
0.284	2.1561160	0.432	0.049	0.179	Endodermis_T0	Endodermis	T0	AT5G02420	SMR3	-0.130	2501	916	2507	2141	-0.006060606	0.006060606
0.733	0.9584491	0.466	0.556	0.110	Endodermis_T0	Endodermis	T0	AT1G56500	SOQ1	0.446	1828	2100	2009	2146	-0.006060606	0.006060606
0.781	0.5398159	0.562	0.569	0.075	Cortex_T0	Cortex	T0	AT3G50840	AT3G50840	0.494	1836	2620	1482	2156	-0.006060606	0.006060606
0.766	1.3164257	0.532	0.783	0.377	Cortex_T0	Cortex	T0	AT1G70310	SPDSYN2	0.406	2410	1976	1713	2228	-0.006060606	0.006060606
0.742	0.5649568	0.484	0.558	0.078	Quiescent Center_T0	Quiescent Center	T0	AT1G14480	AT1G14480	0.480	1948	2178	1960	2251	-0.006060606	0.006060606
0.222	1.1516701	0.556	0.078	0.413	Quiescent Center_T0	Quiescent Center	T0	AT1G28100	AT1G28100	-0.335	2434	1694	2433	2373	-0.006060606	0.006060606
0.722	0.3498067	0.444	0.443	0.097	Endodermis_T0	Endodermis	T0	AT5G54290	CcdA	0.346	2297	2475	2158	2448	0.006060606	0.006060606
0.754	0.8085993	0.508	0.536	0.173	Cortex_T0	Cortex	T0	AT3G18370	SYTF	0.363	2590	2443	1889	2515	-0.006060606	0.006060606
0.703	1.5358949	0.406	0.452	0.097	Cortex_T0	Cortex	T0	AT5G54290	CcdA	0.355	2616	1704	2728	2547	0.006060606	0.006060606
0.735	0.6301417	0.470	0.225	0.016	Cortex_T0	Cortex	T0	AT3G50740	UGT72E1	0.209	2747	2551	2185	2661	0.006060606	0.006060606

genes_to_plt_df <- filter(Clust_Markers, celltype %in% c("Endodermis")) %>%
group_by(stage) %>%
top_n(20, -combined_rank) %>%
top_n(10, abs_long_cor) %>%
ungroup() %>%
arrange(stage)
# genes manually added to plotting list - these are known markers we want to make sure are there
to_add <- filter(Clust_Markers, celltype %in% c("Endodermis")) %>% filter(Name=="SCR" | Name=="MYB36" | Name=="CASP1")

(genes_to_plt_df <- bind_rows(genes_to_plt_df, to_add) %>% 
 ungroup() %>% 
 group_by(gene) %>%
 top_n(1, -combined_rank) %>% # order genes by stage of highest rank
 ungroup() %>% arrange(stage))

genes_to_plt <- unique(genes_to_plt_df$gene)

genes_to_plt_endo <- rev(genes_to_plt) # order to pointing upwards

A tibble: 97 × 17

myAUC	avg_diff	power	pct.1	pct.2	cluster	celltype	stage	gene	Name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank	long_cor	abs_long_cor
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<chr>	<fct>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
0.977	3.347157	0.954	0.997	0.116	Endodermis_T0	Endodermis	T0	AT3G60245	RPL37AC	0.881	85	84	3	7	-0.9151515	0.9151515
0.974	3.623934	0.948	0.997	0.126	Endodermis_T0	Endodermis	T0	AT4G31700	RPS6A	0.871	129	30	26	11	-0.9151515	0.9151515
0.975	3.270026	0.950	0.998	0.107	Endodermis_T0	Endodermis	T0	AT3G56340	RPS26C	0.891	50	104	19	8	-0.8909091	0.8909091
0.977	3.070570	0.954	1.000	0.097	Endodermis_T0	Endodermis	T0	AT5G27850	RPL18C	0.903	15	176	6	13	-0.8909091	0.8909091
0.977	3.254537	0.954	1.000	0.110	Endodermis_T0	Endodermis	T0	AT5G02960	RPS23B	0.890	52	108	4	5	-0.8787879	0.8787879
0.979	3.630642	0.958	1.000	0.132	Endodermis_T0	Endodermis	T0	AT5G02450	RPL36C	0.868	142	28	1	6	-0.8787879	0.8787879
0.975	3.364823	0.950	0.998	0.116	Endodermis_T0	Endodermis	T0	AT1G70600	RPL27AC	0.882	81	79	18	9	-0.8787879	0.8787879
0.971	3.621341	0.942	0.995	0.105	Endodermis_T0	Endodermis	T0	AT2G27530	RPL10AB	0.890	53	31	64	2	-0.8545455	0.8545455
0.977	3.224431	0.954	0.997	0.110	Endodermis_T0	Endodermis	T0	AT3G11510	RPS14B	0.887	64	125	5	12	-0.8303030	0.8303030
0.979	3.320909	0.958	1.000	0.111	Endodermis_T0	Endodermis	T0	AT4G15000	RPL27C	0.889	56	95	2	3	-0.7939394	0.7939394
0.975	3.103580	0.950	0.998	0.096	Endodermis_T0	Endodermis	T0	AT4G16720	RPL15A	0.902	16	163	23	16	-0.7939394	0.7939394
0.975	3.151219	0.950	1.000	0.105	Endodermis_T0	Endodermis	T0	AT5G16130	RPS7C	0.895	35	151	21	17	-0.7939394	0.7939394
0.950	3.353602	0.900	0.975	0.144	Endodermis_T1	Endodermis	T1	AT3G62250	RPS27AC	0.831	36	131	7	10	-0.9272727	0.9272727
0.948	3.404558	0.896	0.942	0.114	Endodermis_T1	Endodermis	T1	AT5G23740	RPS11C	0.828	45	106	25	13	-0.9272727	0.9272727
0.948	3.297031	0.896	0.960	0.118	Endodermis_T1	Endodermis	T1	AT5G15200	RPS9B	0.842	21	155	27	18	-0.9272727	0.9272727
0.950	3.392289	0.900	0.952	0.125	Endodermis_T1	Endodermis	T1	AT3G09200	RPP0B	0.827	47	113	6	7	-0.9151515	0.9151515
0.951	3.355050	0.902	0.955	0.123	Endodermis_T1	Endodermis	T1	AT1G72370	RPSAA	0.832	34	130	4	8	-0.8909091	0.8909091
0.949	3.288305	0.898	0.955	0.122	Endodermis_T1	Endodermis	T1	AT4G17390	RPL15B	0.833	31	157	17	19	-0.8909091	0.8909091
0.949	3.416840	0.898	0.950	0.127	Endodermis_T1	Endodermis	T1	AT3G55750	RPL35AD	0.823	57	102	14	9	-0.8424242	0.8424242
0.945	3.537856	0.890	0.947	0.126	Endodermis_T1	Endodermis	T1	AT1G09590	RPL21A	0.821	60	58	56	12	-0.8424242	0.8424242
0.946	3.492686	0.892	0.930	0.116	Endodermis_T1	Endodermis	T1	AT2G44120	AT2G44120	0.814	72	74	47	16	-0.8424242	0.8424242
0.949	3.351448	0.898	0.950	0.124	Endodermis_T1	Endodermis	T1	AT3G23390	RPL36AB	0.826	51	132	16	17	-0.8424242	0.8424242
0.882	2.216631	0.764	0.960	0.338	Endodermis_T2	Endodermis	T2	AT4G16265	NRPB9B	0.622	19	48	8	9	-0.9393939	0.9393939
0.871	2.125290	0.742	0.955	0.412	Endodermis_T2	Endodermis	T2	AT2G05990	MOD1	0.543	69	53	12	18	-0.9393939	0.9393939
0.868	2.323972	0.736	0.938	0.357	Endodermis_T2	Endodermis	T2	AT2G30200	AT2G30200	0.581	40	38	16	10	-0.9151515	0.9151515
0.892	2.720810	0.784	0.955	0.428	Endodermis_T2	Endodermis	T2	AT1G54580	ACP2	0.527	90	17	6	16	-0.9030303	0.9030303
0.849	2.256817	0.698	0.884	0.274	Endodermis_T2	Endodermis	T2	AT2G43360	BIO2	0.610	25	45	35	15	-0.8909091	0.8909091
0.847	2.385227	0.694	0.899	0.336	Endodermis_T2	Endodermis	T2	AT4G39900	AT4G39900	0.563	57	33	37	17	-0.8909091	0.8909091
0.831	2.315298	0.662	0.722	0.124	Endodermis_T2	Endodermis	T2	AT5G07030	AT5G07030	0.598	32	39	73	20	-0.8666667	0.8666667
0.893	2.446549	0.786	0.922	0.251	Endodermis_T2	Endodermis	T2	AT5G55730	FLA1	0.671	5	31	5	2	-0.8545455	0.8545455
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
0.822	1.576349	0.644	0.797	0.210	Endodermis_T6	Endodermis	T6	AT4G15960	AT4G15960	0.587	2	41	11	2	0.63636364	0.63636364
0.817	1.069673	0.634	0.970	0.427	Endodermis_T6	Endodermis	T6	AT5G57620	MYB36	0.543	12	111	14	26	0.01818182	0.01818182
0.851	1.609812	0.702	0.960	0.527	Endodermis_T7	Endodermis	T7	AT3G06125	AT3G06125	0.433	59	66	1	17	1.00000000	1.00000000
0.849	1.305687	0.698	0.975	0.396	Endodermis_T7	Endodermis	T7	AT1G61590	PBL15	0.579	4	103	3	12	0.98787879	0.98787879
0.806	1.661355	0.612	0.913	0.457	Endodermis_T7	Endodermis	T7	AT3G09270	GSTU8	0.456	44	60	15	15	0.97575758	0.97575758
0.851	1.512009	0.702	0.916	0.240	Endodermis_T7	Endodermis	T7	AT5G66390	PER72	0.676	1	75	2	6	0.92727273	0.92727273
0.782	1.656955	0.564	0.819	0.316	Endodermis_T7	Endodermis	T7	AT4G23493	AT4G23493	0.503	17	62	23	11	0.91515152	0.91515152
0.840	1.908193	0.680	0.900	0.337	Endodermis_T7	Endodermis	T7	AT2G15340	AT2G15340	0.563	7	37	4	1	0.90303030	0.90303030
0.835	1.482905	0.670	0.934	0.365	Endodermis_T7	Endodermis	T7	AT1G35260	MLP165	0.569	6	81	8	10	0.90303030	0.90303030
0.819	2.007340	0.638	0.814	0.330	Endodermis_T7	Endodermis	T7	AT4G34230	CAD5	0.484	27	29	10	3	0.89090909	0.89090909
0.813	1.967599	0.626	0.698	0.139	Endodermis_T7	Endodermis	T7	AT4G21620	AT4G21620	0.559	8	32	11	2	0.85454545	0.85454545
0.810	1.917405	0.620	0.718	0.225	Endodermis_T7	Endodermis	T7	AT4G37790	HAT22	0.493	23	36	13	5	0.84242424	0.84242424
0.840	1.947747	0.680	0.831	0.191	Endodermis_T8	Endodermis	T8	AT3G03520	NPC3	0.640	16	98	31	18	0.91515152	0.91515152
0.837	2.677446	0.674	0.700	0.142	Endodermis_T8	Endodermis	T8	AT1G03850	AT1G03850	0.558	40	16	39	4	0.89090909	0.89090909
0.843	2.014614	0.686	0.862	0.184	Endodermis_T8	Endodermis	T8	AT4G13580	DIR18	0.678	11	85	26	13	0.87878788	0.87878788
0.839	2.596713	0.678	0.675	0.093	Endodermis_T8	Endodermis	T8	AT3G05937	AT3G05937	0.582	25	18	36	2	0.84242424	0.84242424
0.823	2.419715	0.646	0.730	0.167	Endodermis_T8	Endodermis	T8	AT3G14280	AT3G14280	0.563	36	30	66	17	0.83030303	0.83030303
0.851	2.157207	0.702	0.780	0.229	Endodermis_T8	Endodermis	T8	AT5G08240	AT5G08240	0.551	48	66	17	15	0.78181818	0.78181818
0.921	1.955217	0.842	0.987	0.224	Endodermis_T8	Endodermis	T8	AT2G36100	CASP1	0.763	2	95	2	6	0.74545455	0.74545455
0.966	3.437321	0.932	0.976	0.196	Endodermis_T9	Endodermis	T9	AT2G27370	CASP3	0.780	20	69	3	7	0.98787879	0.98787879
0.956	3.540249	0.912	0.967	0.292	Endodermis_T9	Endodermis	T9	AT4G34050	CCoAOMT1	0.675	61	64	6	13	0.98787879	0.98787879
0.909	4.324263	0.818	0.912	0.269	Endodermis_T9	Endodermis	T9	AT5G44790	RAN1	0.643	85	17	47	20	0.98787879	0.98787879
0.963	3.575249	0.926	0.968	0.129	Endodermis_T9	Endodermis	T9	AT1G44970	PER9	0.839	6	60	4	2	0.95151515	0.95151515
0.954	3.021530	0.908	0.969	0.135	Endodermis_T9	Endodermis	T9	AT2G28671	AT2G28671	0.834	7	98	9	8	0.91515152	0.91515152
0.937	3.624695	0.874	0.933	0.203	Endodermis_T9	Endodermis	T9	AT5G65020	ANN2	0.730	35	58	26	10	0.90303030	0.90303030
0.955	3.380457	0.910	0.965	0.104	Endodermis_T9	Endodermis	T9	AT5G06200	CASP4	0.861	1	74	7	5	0.73333333	0.73333333
0.972	3.428775	0.944	0.983	0.122	Endodermis_T9	Endodermis	T9	AT2G39430	DIR9	0.861	2	71	1	4	0.70909091	0.70909091
0.948	3.028116	0.896	0.963	0.115	Endodermis_T9	Endodermis	T9	AT3G11550	CASP2	0.848	3	97	14	9	0.69696970	0.69696970
0.924	3.250986	0.848	0.931	0.100	Endodermis_T9	Endodermis	T9	AT1G71740	AT1G71740	0.831	8	82	38	12	0.69696970	0.69696970
0.893	3.759473	0.786	0.887	0.112	Endodermis_T9	Endodermis	T9	AT2G15300	AT2G15300	0.775	22	49	69	17	0.69696970	0.69696970

genes_to_plt_df <- filter(Clust_Markers, celltype %in% c("Cortex")) %>%
group_by(stage) %>%
top_n(20, -combined_rank) %>%
top_n(10, abs_long_cor) %>%
ungroup() %>%
arrange(stage)

# to add AT4G09760 AT5G02000

to_add <- filter(Clust_Markers, celltype %in% c("Cortex")) %>% filter(gene %in% c("AT1G09750","AT5G07990","AT3G21670","AT4G09760", "AT5G02000", "AT5G64620"))

(genes_to_plt_df <- bind_rows(genes_to_plt_df, to_add) %>% 
 ungroup() %>% 
 group_by(gene) %>%
 top_n(1, -combined_rank) %>%
 ungroup() %>% arrange(stage))



(genes_to_plt_df <- genes_to_plt_df %>% 
 ungroup() %>% 
 group_by(gene) %>%
 top_n(1, -combined_rank) %>%
 ungroup() %>% arrange(stage))

genes_to_plt <- unique(genes_to_plt_df$gene)

genes_to_plt_cortex <- genes_to_plt

A tibble: 91 × 17

myAUC	avg_diff	power	pct.1	pct.2	cluster	celltype	stage	gene	Name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank	long_cor	abs_long_cor
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<chr>	<fct>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
0.962	2.841572	0.924	0.988	0.117	Cortex_T0	Cortex	T0	AT5G28060	RPS24B	0.871	115	178	47	9	-0.8787879	0.8787879
0.967	2.712818	0.934	1.000	0.136	Cortex_T0	Cortex	T0	AT2G27720	AT2G27720	0.864	135	269	5	13	-0.8787879	0.8787879
0.964	2.682942	0.928	0.998	0.124	Cortex_T0	Cortex	T0	AT3G02080	RPS19A	0.874	102	302	19	14	-0.8303030	0.8303030
0.965	2.616760	0.930	0.997	0.106	Cortex_T0	Cortex	T0	AT5G16130	RPS7C	0.891	39	345	14	12	-0.7939394	0.7939394
0.970	2.844078	0.940	1.000	0.096	Cortex_T0	Cortex	T0	AT1G27400	RPL17A	0.904	12	175	1	4	-0.7818182	0.7818182
0.966	2.603655	0.932	1.000	0.096	Cortex_T0	Cortex	T0	AT5G45775	RPL11C	0.904	13	353	9	11	-0.7818182	0.7818182
0.967	2.697931	0.934	1.000	0.102	Cortex_T0	Cortex	T0	AT3G16080	RPL37C	0.898	25	284	6	7	-0.7090909	0.7090909
0.967	2.694637	0.934	0.998	0.093	Cortex_T0	Cortex	T0	AT3G45030	RPS20C	0.905	11	287	7	6	-0.6242424	0.6242424
0.965	2.575404	0.930	0.990	0.105	Cortex_T0	Cortex	T0	AT3G62530	AT3G62530	0.885	65	391	15	17	-0.5878788	0.5878788
0.956	2.843506	0.912	0.971	0.114	Cortex_T0	Cortex	T0	AT3G13580	RPL7D	0.857	158	176	147	20	-0.5636364	0.5636364
0.949	2.995861	0.898	0.970	0.119	Cortex_T1	Cortex	T1	AT2G01250	RPL7B	0.851	42	129	15	5	-0.9272727	0.9272727
0.948	3.017097	0.896	0.964	0.117	Cortex_T1	Cortex	T1	AT5G23740	RPS11C	0.847	54	117	24	6	-0.9272727	0.9272727
0.950	2.797067	0.900	0.988	0.120	Cortex_T1	Cortex	T1	AT5G15200	RPS9B	0.868	15	240	6	17	-0.9272727	0.9272727
0.947	2.813544	0.894	0.985	0.119	Cortex_T1	Cortex	T1	AT4G27090	RPL14B	0.866	18	227	38	18	-0.9151515	0.9151515
0.949	2.975505	0.898	0.964	0.127	Cortex_T1	Cortex	T1	AT1G56045	RPL41G	0.837	74	139	16	10	-0.8909091	0.8909091
0.951	2.812709	0.902	0.988	0.109	Cortex_T1	Cortex	T1	AT5G27850	RPL18C	0.879	4	228	2	12	-0.8909091	0.8909091
0.947	2.950026	0.894	0.979	0.128	Cortex_T1	Cortex	T1	AT3G02560	RPS7B	0.851	44	157	36	13	-0.8909091	0.8909091
0.951	2.775165	0.902	0.988	0.125	Cortex_T1	Cortex	T1	AT3G25520	ATL5	0.863	21	264	3	20	-0.8909091	0.8909091
0.947	2.970321	0.894	0.970	0.118	Cortex_T1	Cortex	T1	AT1G22780	RPS18C	0.852	38	143	35	8	-0.8787879	0.8787879
0.949	2.926049	0.898	0.976	0.125	Cortex_T1	Cortex	T1	AT5G58420	RPS4D	0.851	47	164	17	9	-0.8787879	0.8787879
0.857	2.403694	0.714	0.830	0.247	Cortex_T2	Cortex	T2	AT1G17285	AT1G17285	0.583	7	24	3	4	-0.9878788	0.9878788
0.881	2.655178	0.762	0.878	0.227	Cortex_T2	Cortex	T2	AT5G14020	AT5G14020	0.651	1	9	1	1	-0.9757576	0.9757576
0.809	2.229940	0.618	0.793	0.256	Cortex_T2	Cortex	T2	AT2G24280	AT2G24280	0.537	20	41	34	13	-0.9757576	0.9757576
0.840	2.225051	0.680	0.710	0.165	Cortex_T2	Cortex	T2	AT1G54000	GLL22	0.545	18	42	10	9	-0.9636364	0.9636364
0.831	1.791538	0.662	0.815	0.224	Cortex_T2	Cortex	T2	AT1G32190	AT1G32190	0.591	5	95	15	16	-0.9393939	0.9393939
0.844	2.812988	0.688	0.886	0.406	Cortex_T2	Cortex	T2	AT3G19820	DIM	0.480	66	5	6	11	-0.9151515	0.9151515
0.843	1.745310	0.686	0.862	0.230	Cortex_T2	Cortex	T2	AT1G26945	PRE6	0.632	2	105	7	15	-0.9151515	0.9151515
0.788	2.641667	0.576	0.710	0.211	Cortex_T2	Cortex	T2	AT1G62810	AT1G62810	0.499	43	10	72	18	-0.9030303	0.9030303
0.847	2.965077	0.694	0.700	0.165	Cortex_T2	Cortex	T2	AT2G45050	GATA2	0.535	21	1	5	2	-0.8666667	0.8666667
0.827	2.357117	0.654	0.688	0.164	Cortex_T2	Cortex	T2	AT2G20515	AT2G20515	0.524	29	27	17	10	-0.8666667	0.8666667
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
0.867	1.663836	0.734	0.954	0.345	Cortex_T6	Cortex	T6	AT2G39010	PIP2-6	0.609	2	10	1	1	0.5878788	0.5878788
0.823	1.463082	0.646	0.914	0.343	Cortex_T6	Cortex	T6	AT5G64620	C/VIF2	0.571	6	26	11	2	0.5151515	0.5151515
0.808	1.655623	0.616	0.905	0.403	Cortex_T6	Cortex	T6	AT5G66920	sks17	0.502	19	13	16	4	0.5151515	0.5151515
0.823	1.463082	0.646	0.914	0.343	Cortex_T6	Cortex	T6	AT5G64620	C/VIF2	0.571	6	26	11	2	0.5151515	0.5151515
0.703	1.040975	0.406	0.734	0.282	Cortex_T6	Cortex	T6	AT3G21670	NPF6.4	0.452	51	96	198	121	0.4909091	0.4909091
0.816	1.464443	0.632	0.842	0.272	Cortex_T7	Cortex	T7	AT4G25830	AT4G25830	0.570	6	44	17	6	0.9515152	0.9515152
0.848	1.151143	0.696	0.977	0.382	Cortex_T7	Cortex	T7	AT3G57020	SSL9	0.595	4	80	4	11	0.9515152	0.9515152
0.835	1.257908	0.670	0.915	0.393	Cortex_T7	Cortex	T7	AT4G23400	PIP1-5	0.522	18	70	8	12	0.9515152	0.9515152
0.790	1.334410	0.580	0.793	0.276	Cortex_T7	Cortex	T7	AT4G02700	SULTR3;2	0.517	19	58	34	20	0.9393939	0.9393939
0.816	1.726037	0.632	0.746	0.202	Cortex_T7	Cortex	T7	AT4G00330	CRCK2	0.544	13	21	16	3	0.9272727	0.9272727
0.829	1.136766	0.658	0.937	0.376	Cortex_T7	Cortex	T7	AT3G14690	CYP72A15	0.561	7	82	11	15	0.9272727	0.9272727
0.804	1.752173	0.608	0.720	0.175	Cortex_T7	Cortex	T7	AT5G25810	TINY	0.545	12	18	27	5	0.9151515	0.9151515
0.793	1.969611	0.586	0.666	0.202	Cortex_T7	Cortex	T7	AT1G66725	MIR163	0.464	37	10	33	10	0.9151515	0.9151515
0.863	2.411867	0.726	0.777	0.192	Cortex_T8	Cortex	T8	AT2G31570	GPX2	0.585	24	10	22	3	0.9757576	0.9757576
0.859	2.324442	0.718	0.873	0.344	Cortex_T8	Cortex	T8	AT4G37010	CEN2	0.529	56	19	27	10	0.9636364	0.9636364
0.933	2.219710	0.866	0.976	0.181	Cortex_T8	Cortex	T8	AT5G15180	PER56	0.795	1	36	1	1	0.9515152	0.9515152
0.838	2.193481	0.676	0.834	0.170	Cortex_T8	Cortex	T8	AT5G44130	FLA13	0.664	8	43	38	9	0.9515152	0.9515152
0.895	1.943125	0.790	0.965	0.311	Cortex_T8	Cortex	T8	AT5G53370	ATPMEPCRF	0.654	10	93	7	12	0.9515152	0.9515152
0.875	1.967181	0.750	0.820	0.180	Cortex_T8	Cortex	T8	AT5G63600	FLS5	0.640	13	87	15	16	0.9515152	0.9515152
0.835	2.402254	0.670	0.797	0.274	Cortex_T8	Cortex	T8	AT3G28940	AIG2B	0.523	61	11	44	18	0.9515152	0.9515152
0.792	2.103887	0.584	0.719	0.199	Cortex_T8	Cortex	T8	AT4G09760	AT4G09760	0.520	63	61	95	45	0.6484848	0.6484848
0.885	3.748602	0.770	0.928	0.335	Cortex_T9	Cortex	T9	AT3G48890	MSBP2	0.593	49	1	26	7	0.9878788	0.9878788
0.920	2.949297	0.840	0.948	0.237	Cortex_T9	Cortex	T9	AT5G19230	AT5G19230	0.711	8	28	11	4	0.9757576	0.9757576
0.878	2.891494	0.756	0.873	0.199	Cortex_T9	Cortex	T9	AT3G18170	AT3G18170	0.674	19	32	33	11	0.9757576	0.9757576
0.888	2.832023	0.776	0.796	0.189	Cortex_T9	Cortex	T9	AT4G25900	AT4G25900	0.607	40	34	24	14	0.9757576	0.9757576
0.929	2.659444	0.858	0.958	0.203	Cortex_T9	Cortex	T9	AT5G44380	AT5G44380	0.755	5	61	6	6	0.9515152	0.9515152
0.872	2.820674	0.744	0.865	0.146	Cortex_T9	Cortex	T9	AT2G27920	SCPL51	0.719	7	35	35	9	0.9515152	0.9515152
0.880	3.077648	0.760	0.865	0.239	Cortex_T9	Cortex	T9	AT4G15610	AT4G15610	0.626	31	19	30	10	0.9515152	0.9515152
0.871	3.186065	0.742	0.921	0.347	Cortex_T9	Cortex	T9	AT3G17420	GPK1	0.574	56	10	36	15	0.9515152	0.9515152
0.894	2.733063	0.788	0.967	0.420	Cortex_T9	Cortex	T9	AT1G08930	ERD6	0.547	75	43	20	20	0.9515152	0.9515152

A tibble: 91 × 17

myAUC	avg_diff	power	pct.1	pct.2	cluster	celltype	stage	gene	Name	pct.diff	pct.diff_rank	avg_diff_rank	myAUC_rank	combined_rank	long_cor	abs_long_cor
<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>	<chr>	<fct>	<chr>	<chr>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
0.962	2.841572	0.924	0.988	0.117	Cortex_T0	Cortex	T0	AT5G28060	RPS24B	0.871	115	178	47	9	-0.8787879	0.8787879
0.967	2.712818	0.934	1.000	0.136	Cortex_T0	Cortex	T0	AT2G27720	AT2G27720	0.864	135	269	5	13	-0.8787879	0.8787879
0.964	2.682942	0.928	0.998	0.124	Cortex_T0	Cortex	T0	AT3G02080	RPS19A	0.874	102	302	19	14	-0.8303030	0.8303030
0.965	2.616760	0.930	0.997	0.106	Cortex_T0	Cortex	T0	AT5G16130	RPS7C	0.891	39	345	14	12	-0.7939394	0.7939394
0.970	2.844078	0.940	1.000	0.096	Cortex_T0	Cortex	T0	AT1G27400	RPL17A	0.904	12	175	1	4	-0.7818182	0.7818182
0.966	2.603655	0.932	1.000	0.096	Cortex_T0	Cortex	T0	AT5G45775	RPL11C	0.904	13	353	9	11	-0.7818182	0.7818182
0.967	2.697931	0.934	1.000	0.102	Cortex_T0	Cortex	T0	AT3G16080	RPL37C	0.898	25	284	6	7	-0.7090909	0.7090909
0.967	2.694637	0.934	0.998	0.093	Cortex_T0	Cortex	T0	AT3G45030	RPS20C	0.905	11	287	7	6	-0.6242424	0.6242424
0.965	2.575404	0.930	0.990	0.105	Cortex_T0	Cortex	T0	AT3G62530	AT3G62530	0.885	65	391	15	17	-0.5878788	0.5878788
0.956	2.843506	0.912	0.971	0.114	Cortex_T0	Cortex	T0	AT3G13580	RPL7D	0.857	158	176	147	20	-0.5636364	0.5636364
0.949	2.995861	0.898	0.970	0.119	Cortex_T1	Cortex	T1	AT2G01250	RPL7B	0.851	42	129	15	5	-0.9272727	0.9272727
0.948	3.017097	0.896	0.964	0.117	Cortex_T1	Cortex	T1	AT5G23740	RPS11C	0.847	54	117	24	6	-0.9272727	0.9272727
0.950	2.797067	0.900	0.988	0.120	Cortex_T1	Cortex	T1	AT5G15200	RPS9B	0.868	15	240	6	17	-0.9272727	0.9272727
0.947	2.813544	0.894	0.985	0.119	Cortex_T1	Cortex	T1	AT4G27090	RPL14B	0.866	18	227	38	18	-0.9151515	0.9151515
0.949	2.975505	0.898	0.964	0.127	Cortex_T1	Cortex	T1	AT1G56045	RPL41G	0.837	74	139	16	10	-0.8909091	0.8909091
0.951	2.812709	0.902	0.988	0.109	Cortex_T1	Cortex	T1	AT5G27850	RPL18C	0.879	4	228	2	12	-0.8909091	0.8909091
0.947	2.950026	0.894	0.979	0.128	Cortex_T1	Cortex	T1	AT3G02560	RPS7B	0.851	44	157	36	13	-0.8909091	0.8909091
0.951	2.775165	0.902	0.988	0.125	Cortex_T1	Cortex	T1	AT3G25520	ATL5	0.863	21	264	3	20	-0.8909091	0.8909091
0.947	2.970321	0.894	0.970	0.118	Cortex_T1	Cortex	T1	AT1G22780	RPS18C	0.852	38	143	35	8	-0.8787879	0.8787879
0.949	2.926049	0.898	0.976	0.125	Cortex_T1	Cortex	T1	AT5G58420	RPS4D	0.851	47	164	17	9	-0.8787879	0.8787879
0.857	2.403694	0.714	0.830	0.247	Cortex_T2	Cortex	T2	AT1G17285	AT1G17285	0.583	7	24	3	4	-0.9878788	0.9878788
0.881	2.655178	0.762	0.878	0.227	Cortex_T2	Cortex	T2	AT5G14020	AT5G14020	0.651	1	9	1	1	-0.9757576	0.9757576
0.809	2.229940	0.618	0.793	0.256	Cortex_T2	Cortex	T2	AT2G24280	AT2G24280	0.537	20	41	34	13	-0.9757576	0.9757576
0.840	2.225051	0.680	0.710	0.165	Cortex_T2	Cortex	T2	AT1G54000	GLL22	0.545	18	42	10	9	-0.9636364	0.9636364
0.831	1.791538	0.662	0.815	0.224	Cortex_T2	Cortex	T2	AT1G32190	AT1G32190	0.591	5	95	15	16	-0.9393939	0.9393939
0.844	2.812988	0.688	0.886	0.406	Cortex_T2	Cortex	T2	AT3G19820	DIM	0.480	66	5	6	11	-0.9151515	0.9151515
0.843	1.745310	0.686	0.862	0.230	Cortex_T2	Cortex	T2	AT1G26945	PRE6	0.632	2	105	7	15	-0.9151515	0.9151515
0.788	2.641667	0.576	0.710	0.211	Cortex_T2	Cortex	T2	AT1G62810	AT1G62810	0.499	43	10	72	18	-0.9030303	0.9030303
0.847	2.965077	0.694	0.700	0.165	Cortex_T2	Cortex	T2	AT2G45050	GATA2	0.535	21	1	5	2	-0.8666667	0.8666667
0.827	2.357117	0.654	0.688	0.164	Cortex_T2	Cortex	T2	AT2G20515	AT2G20515	0.524	29	27	17	10	-0.8666667	0.8666667
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
0.867	1.663836	0.734	0.954	0.345	Cortex_T6	Cortex	T6	AT2G39010	PIP2-6	0.609	2	10	1	1	0.5878788	0.5878788
0.823	1.463082	0.646	0.914	0.343	Cortex_T6	Cortex	T6	AT5G64620	C/VIF2	0.571	6	26	11	2	0.5151515	0.5151515
0.808	1.655623	0.616	0.905	0.403	Cortex_T6	Cortex	T6	AT5G66920	sks17	0.502	19	13	16	4	0.5151515	0.5151515
0.823	1.463082	0.646	0.914	0.343	Cortex_T6	Cortex	T6	AT5G64620	C/VIF2	0.571	6	26	11	2	0.5151515	0.5151515
0.703	1.040975	0.406	0.734	0.282	Cortex_T6	Cortex	T6	AT3G21670	NPF6.4	0.452	51	96	198	121	0.4909091	0.4909091
0.816	1.464443	0.632	0.842	0.272	Cortex_T7	Cortex	T7	AT4G25830	AT4G25830	0.570	6	44	17	6	0.9515152	0.9515152
0.848	1.151143	0.696	0.977	0.382	Cortex_T7	Cortex	T7	AT3G57020	SSL9	0.595	4	80	4	11	0.9515152	0.9515152
0.835	1.257908	0.670	0.915	0.393	Cortex_T7	Cortex	T7	AT4G23400	PIP1-5	0.522	18	70	8	12	0.9515152	0.9515152
0.790	1.334410	0.580	0.793	0.276	Cortex_T7	Cortex	T7	AT4G02700	SULTR3;2	0.517	19	58	34	20	0.9393939	0.9393939
0.816	1.726037	0.632	0.746	0.202	Cortex_T7	Cortex	T7	AT4G00330	CRCK2	0.544	13	21	16	3	0.9272727	0.9272727
0.829	1.136766	0.658	0.937	0.376	Cortex_T7	Cortex	T7	AT3G14690	CYP72A15	0.561	7	82	11	15	0.9272727	0.9272727
0.804	1.752173	0.608	0.720	0.175	Cortex_T7	Cortex	T7	AT5G25810	TINY	0.545	12	18	27	5	0.9151515	0.9151515
0.793	1.969611	0.586	0.666	0.202	Cortex_T7	Cortex	T7	AT1G66725	MIR163	0.464	37	10	33	10	0.9151515	0.9151515
0.863	2.411867	0.726	0.777	0.192	Cortex_T8	Cortex	T8	AT2G31570	GPX2	0.585	24	10	22	3	0.9757576	0.9757576
0.859	2.324442	0.718	0.873	0.344	Cortex_T8	Cortex	T8	AT4G37010	CEN2	0.529	56	19	27	10	0.9636364	0.9636364
0.933	2.219710	0.866	0.976	0.181	Cortex_T8	Cortex	T8	AT5G15180	PER56	0.795	1	36	1	1	0.9515152	0.9515152
0.838	2.193481	0.676	0.834	0.170	Cortex_T8	Cortex	T8	AT5G44130	FLA13	0.664	8	43	38	9	0.9515152	0.9515152
0.895	1.943125	0.790	0.965	0.311	Cortex_T8	Cortex	T8	AT5G53370	ATPMEPCRF	0.654	10	93	7	12	0.9515152	0.9515152
0.875	1.967181	0.750	0.820	0.180	Cortex_T8	Cortex	T8	AT5G63600	FLS5	0.640	13	87	15	16	0.9515152	0.9515152
0.835	2.402254	0.670	0.797	0.274	Cortex_T8	Cortex	T8	AT3G28940	AIG2B	0.523	61	11	44	18	0.9515152	0.9515152
0.792	2.103887	0.584	0.719	0.199	Cortex_T8	Cortex	T8	AT4G09760	AT4G09760	0.520	63	61	95	45	0.6484848	0.6484848
0.885	3.748602	0.770	0.928	0.335	Cortex_T9	Cortex	T9	AT3G48890	MSBP2	0.593	49	1	26	7	0.9878788	0.9878788
0.920	2.949297	0.840	0.948	0.237	Cortex_T9	Cortex	T9	AT5G19230	AT5G19230	0.711	8	28	11	4	0.9757576	0.9757576
0.878	2.891494	0.756	0.873	0.199	Cortex_T9	Cortex	T9	AT3G18170	AT3G18170	0.674	19	32	33	11	0.9757576	0.9757576
0.888	2.832023	0.776	0.796	0.189	Cortex_T9	Cortex	T9	AT4G25900	AT4G25900	0.607	40	34	24	14	0.9757576	0.9757576
0.929	2.659444	0.858	0.958	0.203	Cortex_T9	Cortex	T9	AT5G44380	AT5G44380	0.755	5	61	6	6	0.9515152	0.9515152
0.872	2.820674	0.744	0.865	0.146	Cortex_T9	Cortex	T9	AT2G27920	SCPL51	0.719	7	35	35	9	0.9515152	0.9515152
0.880	3.077648	0.760	0.865	0.239	Cortex_T9	Cortex	T9	AT4G15610	AT4G15610	0.626	31	19	30	10	0.9515152	0.9515152
0.871	3.186065	0.742	0.921	0.347	Cortex_T9	Cortex	T9	AT3G17420	GPK1	0.574	56	10	36	15	0.9515152	0.9515152
0.894	2.733063	0.788	0.967	0.420	Cortex_T9	Cortex	T9	AT1G08930	ERD6	0.547	75	43	20	20	0.9515152	0.9515152

write.csv(genes_to_plt_endo, "./supp_data/consensus_time_endo_to_plot.csv", row.names=F)
write.csv(genes_to_plt_cortex, "./supp_data/consensus_time_cortex_to_plot.csv", row.names=F)

# subset for endodermis
Endo_QC  <- subset(rc.integrated, celltype.anno %in% c("Quiescent Center", "Endodermis"))

# pseudobulk of each stage of endodermis


  afm <- as.matrix(Endo_QC@assays$integrated@data)
  pooled_endo <- matrix(nrow=nrow(afm), ncol = 0)

  for (i in unique(Endo_QC@meta.data$consensus.time.group)) {
    m <- afm[,which(Endo_QC@meta.data$consensus.time.group==i)]
    pooled_endo <- cbind(pooled_endo, rowSums(m)/ncol(m))
  }

colnames(pooled_endo) <- unique(Endo_QC@meta.data$consensus.time.group)

(endo_sub_m <- pooled_endo[genes_to_plt_endo, c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9")])

A matrix: 94 × 10 of type dbl

	T0	T1	T2	T3	T4	T5	T6	T7	T8	T9
AT2G15300	-0.14242723	-0.16149722	-2.809425e-01	-0.33057376	-0.31115196	-0.27073994	0.09957142	0.42311098	1.9529662	6.69348303
AT1G71740	-0.35660894	-0.38861004	-4.704698e-01	-0.45982226	-0.44527083	-0.44129964	-0.28372146	1.69033522	5.1668984	8.56355676
AT3G11550	-0.45387346	-0.47447793	-5.831723e-01	-0.57034652	-0.53650431	-0.49510965	-0.02590912	3.19715514	6.8916465	9.19329727
AT2G39430	-0.44548211	-0.40422254	-5.372530e-01	-0.51568935	-0.50679208	-0.49683335	-0.20074373	1.85296777	5.2198797	9.55926738
AT5G06200	-0.32523428	-0.41466255	-5.043669e-01	-0.46422293	-0.47391101	-0.43037394	-0.28721650	1.00965944	4.5592723	9.10937747
AT5G65020	-1.00819942	-0.69093475	-3.756301e-01	-0.30468028	-0.34045204	-0.14226229	0.31271816	1.29149919	2.0227179	3.89940521
AT2G28671	-0.35460516	-0.36516862	-3.968545e-01	-0.38767272	-0.32261326	0.03630028	0.85361903	3.40586000	5.8363485	8.06132472
AT1G44970	-0.28789572	-0.28097155	-3.083765e-01	-0.18732854	-0.15816977	-0.08031299	0.11728710	1.19911422	3.4993281	8.67665303
AT5G44790	-0.76083064	-0.81538100	-7.564502e-01	-0.69014925	-0.63201968	-0.47639592	-0.24766409	0.08388293	0.7781348	4.31773659
AT4G34050	-1.78984712	-1.90736562	-6.352310e-01	-0.14155373	0.25091423	0.90158277	1.93458694	1.12839587	0.9368397	8.30696836
AT2G27370	-0.50029044	-0.50486636	-1.774493e-01	-0.04975765	0.20792688	0.52270686	0.86990819	1.21404431	3.1665410	9.37294065
AT2G36100	-0.51355613	-0.73281177	-9.928055e-01	-0.95666284	-0.91629516	-0.56717615	2.45439191	7.79884294	9.6092401	9.95081880
AT5G08240	-0.27700742	-0.34189126	-2.553061e-01	-0.30848236	-0.12210478	0.18298834	0.99379532	2.60528658	3.2669372	0.30265366
AT3G14280	-0.72448509	-0.94559051	-9.690169e-01	-0.98463245	-0.88933492	-0.72934232	-0.01771861	2.56127653	3.4632678	0.96909351
AT3G05937	-0.36738608	-0.46762040	-4.958722e-01	-0.47195996	-0.46186238	-0.45072244	-0.11008450	1.34889622	1.6747880	-0.10704136
AT4G13580	-0.48109373	-0.59546225	-5.659305e-01	-0.53690441	-0.50600947	-0.44874098	0.18975921	4.57491933	7.7580452	8.91861860
AT1G03850	-1.16423041	-1.25070429	-1.348715e+00	-1.44590686	-1.45407896	-1.40485683	-0.55767638	3.27082006	4.6186920	-0.32723453
AT3G03520	-0.59844104	-0.68936662	-5.969454e-01	-0.58591014	-0.49933465	-0.19306077	0.55340479	1.40525730	2.1205917	2.87664476
AT4G37790	-0.66752120	-0.79582544	-6.866044e-01	-0.71057285	-0.24879277	0.82081914	2.38442758	3.14958774	3.4099629	0.03788676
AT4G21620	-0.05369942	-0.07590941	-7.095406e-02	-0.07692939	-0.07828367	-0.05389582	0.02134841	0.53912513	0.6530873	0.14042561
AT4G34230	-1.06191982	-1.39337138	-1.308467e+00	-1.11219673	-0.49651345	1.24639547	2.99859374	2.96681343	2.4773615	0.87865701
AT1G35260	-0.49361851	-0.56419115	5.535556e-01	2.02711131	2.67586303	3.13618595	3.33295066	4.34727150	4.9797674	2.18027557
AT2G15340	-0.24379921	-0.19570308	7.277600e-02	0.16201485	0.55413515	1.44069699	2.99404419	4.10444530	3.5684940	0.31115815
AT4G23493	-0.16169833	-0.05200886	1.979299e-01	0.25499557	0.32146983	0.50622072	0.81983407	1.32412540	1.5062300	0.83188347
AT5G66390	-0.75829852	-0.83186349	-7.653330e-01	-0.73368548	-0.43265705	0.98855808	4.51154625	7.99520385	9.2026198	9.79796924
AT3G09270	-0.97914137	-0.92688327	-5.706325e-01	0.09600136	0.89512897	1.68951062	2.36409743	3.53342921	3.8430070	2.42631678
AT3G06125	-0.60443069	-0.43269097	8.743106e-05	0.49842413	0.85137943	1.23874191	2.33089604	4.74458252	5.7413495	4.26022675
AT4G15960	-0.58179070	-0.68583456	-3.155279e-01	0.14496414	0.72251809	1.45006409	1.73112141	0.91634507	0.6757686	0.38246189
AT3G13520	-1.37220147	-1.03789693	1.532634e+00	2.16087968	2.90593199	3.91355324	4.44137629	4.32409005	4.1391877	0.75889705
AT3G48350	-0.26788403	-0.23252882	-6.986186e-02	0.03060283	0.57255740	1.47843302	2.05287986	0.63768838	0.1729415	-0.08603755
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
AT5G55730	0.7301355	0.8090575	2.5678981	2.0777407	1.2113615	0.18219065	-0.42325776	-0.62789472	-0.7088659	-0.6319220
AT5G07030	2.8050195	1.5060868	2.1289931	0.4334027	-0.4513561	-0.88581173	-1.01038801	-0.93587124	-0.9431494	-0.8522577
AT4G39900	0.4004057	1.3886226	2.4839831	1.8592118	0.9826779	0.36228791	0.05387223	-0.14073418	-0.2418469	-0.3653124
AT2G43360	1.2852430	2.3943542	2.0400653	0.9542262	0.3744483	-0.04831887	-0.23456547	-0.32029943	-0.3186371	-0.1348833
AT1G54580	2.4487875	4.0945902	5.5231830	3.5103594	1.5934715	0.13717758	-0.42405624	-0.73240974	-0.9145339	-0.8148472
AT2G30200	1.7032993	2.2281979	2.6501361	1.5612248	0.7421746	0.05982531	-0.34158985	-0.48462647	-0.5185469	-0.4150099
AT2G05990	2.3496195	3.3683938	3.8579667	2.3118050	1.1542319	0.23505782	-0.10356638	-0.44658022	-0.6600482	-0.4646967
AT4G16265	1.1230050	1.6492551	2.4688246	1.8431108	1.4007037	0.97757805	0.53250412	0.01904131	-0.2006307	-0.2027613
AT3G23390	6.2142828	4.5596509	-0.4297028	-1.0822531	-1.4014637	-1.58358349	-1.60722642	-1.47562475	-1.4656884	-1.2936825
AT2G44120	5.3386589	3.9173700	-0.5394960	-0.9866326	-1.1646553	-1.28440055	-1.31444509	-1.22345072	-1.1814329	-1.0534027
AT1G09590	5.8605227	4.4474440	-0.3573291	-0.9283873	-1.1598000	-1.29736320	-1.29019645	-1.10303056	-1.0810291	-0.9349805
AT3G55750	6.0717685	4.4427731	-0.3726595	-0.9141344	-1.1537023	-1.27974321	-1.31503654	-1.13260243	-1.1334568	-1.0689091
AT4G17390	5.4276392	3.8748100	-0.3934553	-0.9273360	-1.1425201	-1.29053841	-1.31350804	-1.21653273	-1.2237207	-1.1653592
AT1G72370	6.2600967	4.5042008	-0.4146206	-1.0762066	-1.3686621	-1.54143467	-1.53334547	-1.41357058	-1.3910397	-1.3553782
AT3G09200	6.2909812	4.4450343	-0.2888330	-0.8700513	-1.1717659	-1.31294716	-1.36872318	-1.22467377	-1.2697066	-1.2465645
AT5G15200	5.3735738	4.0782352	-0.5067444	-1.0256581	-1.2427727	-1.35934329	-1.40214375	-1.31463833	-1.3516948	-1.2932665
AT5G23740	5.8488341	4.4577998	-0.5121212	-1.0166777	-1.2135075	-1.35567896	-1.40429675	-1.36583655	-1.3702588	-1.2953544
AT3G62250	5.2396468	4.0852979	-0.1247129	-0.7419270	-1.0405571	-1.19937011	-1.19944894	-1.01356115	-1.0188779	-1.0842226
AT5G16130	8.7414625	5.3445527	-0.5962779	-1.3774850	-1.7005758	-1.89748813	-1.92982673	-1.77826124	-1.7345597	-1.6766437
AT4G16720	7.5221448	4.9888458	-0.8093254	-1.4384086	-1.7087000	-1.85370291	-1.84689724	-1.68557445	-1.6392852	-1.6203207
AT4G15000	6.8672763	4.6417626	-0.4740812	-1.2327746	-1.5330334	-1.67733837	-1.67124782	-1.47479762	-1.3968493	-1.2754481
AT3G11510	7.2585245	5.0546565	-0.5311614	-1.3338677	-1.6654568	-1.85770725	-1.88197263	-1.74540058	-1.7073625	-1.6488258
AT2G27530	4.1757220	2.9452826	-0.6267409	-1.0264489	-1.1620509	-1.18784162	-1.15379127	-1.01715070	-1.0508467	-1.1665010
AT1G70600	5.7410246	4.2510042	-0.3639923	-1.1240070	-1.4462404	-1.62412733	-1.64096479	-1.46608023	-1.4374359	-1.4353937
AT5G02450	4.9093378	3.3948316	-0.2406399	-0.7834026	-1.0925696	-1.23727379	-1.23249726	-1.11710542	-1.0882024	-1.0854760
AT5G02960	8.1821150	5.4831365	-0.4194876	-1.3335844	-1.7448029	-1.97707080	-2.01581882	-1.86506820	-1.8779279	-1.8504664
AT5G27850	6.3158525	4.3083371	-0.7171787	-1.2037148	-1.4211263	-1.55899730	-1.59421486	-1.51407829	-1.5174841	-1.4320561
AT3G56340	5.8095825	4.0063386	-0.5257235	-1.2267011	-1.5099342	-1.64956692	-1.68118999	-1.59981529	-1.5516093	-1.5311076
AT4G31700	4.7459831	3.4037189	-0.1995999	-0.8129013	-1.0987796	-1.25886305	-1.25695927	-1.14372572	-1.1487195	-1.1171770
AT3G60245	7.0449274	4.9107791	-0.4235197	-1.1278805	-1.4158444	-1.59340172	-1.58224810	-1.40767588	-1.4079721	-1.4325733

# quantile normalize

mat = endo_sub_m
mat2 = t(apply(mat, 1, function(x) {
    q10 = quantile(x, 0.1)
    q90 = quantile(x, 0.9)
    x[x < q10] = q10
    x[x > q90] = q90
    scale(x)
}))
colnames(mat2) = colnames(mat)

# side annotation with color to match umap

endo_sa = rowAnnotation(foo = anno_block(gp = gpar(fill = "#0000FF"),
        labels = c("Endodermis"), 
        labels_gp = gpar(col = "white", fontsize = 18)))

# genes to mark on side of heatmap

(endo_mat_for_mark <- data.frame(mat2) %>% 
 rownames_to_column("gene") %>%
 left_join(feature_names) %>% 
 mutate(index=dplyr::row_number()) %>%
select(Name, index) %>%
filter(Name %in% c("XTH16", "SCR", "MYB36", "CASP1", "CASP2", "CASP3")))

Joining, by = "gene"

A data.frame: 6 × 2

Name	index
<chr>	<int>
CASP2	3
CASP3	11
CASP1	12
MYB36	36
SCR	61
XTH16	63

# at - index of genes you want to mark
# labels - Names to show

endo_mark_anno = rowAnnotation(foo = anno_mark(at = endo_mat_for_mark$index, labels = endo_mat_for_mark$Name, labels_gp = gpar(col = "black", fontsize = 16)))

options(repr.plot.width=10, repr.plot.height=8)
Endo_hm <- Heatmap(mat2, 
        col = colorRamp2(c(-1.5, 0, 1.5), 
                       c('blue','white','red')), 
                show_column_names = T,
                cluster_columns = F,
        cluster_rows=F,
                show_row_names = F, 
                   left_annotation=endo_sa,
                   right_annotation=endo_mark_anno,
               heatmap_legend_param = list(title_position="topcenter", title = "Expression", direction="horizontal"))

draw(Endo_hm, padding = unit(c(10, 10, 10, 10), "mm"), heatmap_legend_side = "top")

# cortex subset
Cor_QC  <- subset(rc.integrated, celltype.anno %in% c("Quiescent Center", "Cortex"))

# pseudobulk of each stage of cortex


  afm <- as.matrix(Cor_QC@assays$integrated@data)
  pooled_cortex <- matrix(nrow=nrow(afm), ncol = 0)

  for (i in unique(Cor_QC@meta.data$consensus.time.group)) {
    m <- afm[,which(Cor_QC@meta.data$consensus.time.group==i)]
    pooled_cortex <- cbind(pooled_cortex, rowSums(m)/ncol(m))
  }

colnames(pooled_cortex) <- unique(Cor_QC@meta.data$consensus.time.group)

(cor_sub_m <- pooled_cortex[genes_to_plt_cortex, c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9")])

A matrix: 90 × 10 of type dbl

	T0	T1	T2	T3	T4	T5	T6	T7	T8	T9
AT5G28060	3.3618492	2.6299378	-0.073066518	-0.8474143	-0.98969054	-1.1703605	-1.268575179	-1.31155911	-1.37317022	-1.40318396
AT2G27720	3.8613899	3.0233971	0.134892414	-0.5785870	-0.74875052	-0.8763586	-1.070331353	-1.12689923	-1.16017116	-1.20657080
AT3G02080	3.4246586	2.7404931	-0.059228205	-0.7242290	-0.82861098	-0.9228851	-1.013056434	-1.03356621	-1.04725742	-1.10604766
AT5G16130	7.8747834	5.6665560	-0.104457378	-1.3917178	-1.58696732	-1.7622831	-1.893205932	-1.86407705	-1.87133041	-1.90693716
AT1G27400	8.6212721	5.7445302	-0.393651039	-1.6408461	-1.82125725	-1.9811820	-2.067151010	-2.07150361	-2.10114890	-2.11542489
AT5G45775	7.5149917	5.1948935	-0.131228698	-1.3315355	-1.54532984	-1.6877456	-1.770982082	-1.75838575	-1.77343643	-1.80507639
AT3G16080	8.1424800	5.6297957	0.097099207	-1.4047155	-1.67514368	-1.8402602	-1.932430762	-1.89338502	-1.93443066	-1.95262379
AT3G45030	7.8812068	5.2848012	-0.604960919	-1.5217063	-1.62330267	-1.6821780	-1.734441332	-1.72134059	-1.74396387	-1.79158357
AT3G62530	6.6559972	5.0625430	-0.018679339	-0.9486519	-1.04881295	-1.1142400	-1.170516840	-1.14192258	-1.07853332	-1.06305724
AT3G13580	3.1904841	2.3151531	-0.244782986	-0.5234968	-0.58842122	-0.6335214	-0.690727363	-0.62127948	-0.64374928	-0.68322709
AT2G01250	5.9062186	4.8376718	-0.042753974	-1.0671940	-1.26087097	-1.4006699	-1.520077185	-1.55486651	-1.56722159	-1.60753480
AT5G23740	5.2427375	4.4274909	-0.187092773	-1.0925899	-1.27987082	-1.4002439	-1.484831185	-1.50604150	-1.54163399	-1.57561474
AT5G15200	4.7821580	4.0877754	0.078056386	-0.9816799	-1.15238643	-1.3348708	-1.434427549	-1.49591857	-1.53615015	-1.60086711
AT4G27090	5.3219905	4.4249431	0.006255397	-1.1296623	-1.32381567	-1.5512118	-1.701991513	-1.75823099	-1.81293531	-1.87016210
AT1G56045	4.1265461	3.8013931	0.081949902	-0.7611507	-1.00883428	-1.1740414	-1.305814679	-1.32739749	-1.36705708	-1.40193826
AT5G27850	4.9486069	4.3240707	-0.191237039	-1.1429370	-1.29665658	-1.4495947	-1.568391609	-1.58415006	-1.62066850	-1.66361449
AT3G02560	4.1414938	3.6324283	0.114630934	-0.8004553	-0.99355820	-1.1443436	-1.261804350	-1.27392442	-1.32918619	-1.35199266
AT3G25520	4.6023274	4.0387473	-0.017835266	-0.9581989	-1.13737805	-1.3189952	-1.458921665	-1.51155934	-1.53587220	-1.57481806
AT1G22780	5.4168899	4.3761691	0.005704120	-1.0896915	-1.22533436	-1.3521492	-1.452643774	-1.45722806	-1.48745400	-1.54190511
AT5G58420	6.1514813	5.0818177	0.060803337	-0.9963555	-1.21786683	-1.3621342	-1.486700552	-1.53436320	-1.56955572	-1.59090436
AT1G17285	1.6325749	2.4238939	2.946607675	1.4195383	0.99698094	0.2849380	-0.222638715	-0.55796075	-0.75473131	-0.78760903
AT5G14020	0.9988840	1.5719543	2.589507503	1.7451860	1.50660433	0.7071862	0.001890851	-0.61821259	-0.82081396	-0.88111036
AT2G24280	0.8123856	1.2868378	1.748959390	0.9226257	0.71953032	0.3508008	0.130061805	-0.02054685	-0.08388366	-0.07564624
AT1G54000	0.6831540	0.6413816	1.824268765	1.2858661	0.70363342	0.0130056	-1.025801594	-1.50244121	-1.74456840	-1.63801288
AT1G32190	2.4876543	2.6718260	2.117170382	1.3912299	0.98882962	0.3993983	-0.180577525	-0.35945728	-0.34673378	-0.34883379
AT3G19820	1.7004443	2.7697936	4.161620765	2.8243545	1.90265264	1.1560632	-0.064870255	-0.68281676	-0.92049556	-0.98320564
AT1G26945	0.1855114	0.7098123	1.946718754	1.6403748	1.40327453	0.7383544	-0.053394961	-0.43028766	-0.48110573	-0.49928227
AT1G62810	0.5479245	1.0322627	1.607207351	0.6944904	0.42579520	0.2197674	-0.061714949	-0.10569089	-0.04563733	0.04739972
AT2G45050	1.6793998	2.6796167	3.433404661	0.9839113	0.10362365	-0.8877786	-1.285458636	-1.33661885	-1.35485756	-1.35696932
AT2G20515	2.8798592	3.4300523	2.393420845	0.5070670	-0.01738539	-0.4343897	-0.621503108	-0.64727459	-0.64181123	-0.64228395
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
AT4G30190	-0.8222151	-0.71308906	0.547734155	1.9185150861	2.153623989	2.774670662	2.721294556	2.1574153	1.5172489	1.6990466
AT2G39010	-0.8139579	-0.67626689	2.026462580	4.0015520971	5.126804381	6.287168344	6.894412728	4.8370105	2.7223877	1.8985461
AT5G64620	-0.4716171	-0.43842360	1.371077821	3.6439026942	4.676059864	5.523951227	5.628490676	3.9425602	2.2505642	2.2379515
AT5G66920	-0.7473664	-0.65055467	0.553446198	2.1486002344	2.462291078	3.186697917	3.302314359	2.4521441	1.1240185	0.9309736
AT3G21670	-0.2335829	0.04102029	1.529240304	2.2943438276	2.562045900	2.404527983	2.326697404	1.6644404	0.9748708	1.2192169
AT4G25830	-0.8471250	-0.91139702	-0.567159093	-0.2072573097	0.064841310	0.555590275	1.600543257	3.1054826	3.7737804	4.0987657
AT3G57020	-1.3261087	-1.28628942	0.019712843	1.5809823490	1.951262142	2.852295726	3.904178517	5.8760458	7.1078994	8.0188040
AT4G23400	-1.6189207	-1.63947830	-1.025042277	-0.0532276043	0.501373587	1.352312457	3.374835389	5.6359847	6.9700095	7.6733581
AT4G02700	-0.2927995	-0.32901222	-0.104593336	0.0796793157	-0.008197751	0.168664647	0.570563028	1.6260180	2.1607804	2.6013351
AT4G00330	-0.5142283	-0.58494483	-0.528194500	-0.3654327146	-0.360308224	-0.212730648	0.379753767	1.3213993	1.4932740	1.0615307
AT3G14690	-0.4059807	-0.26135001	0.072725588	0.4143861589	0.564499296	0.833333701	1.953636202	3.9122874	4.6568164	6.0095359
AT5G25810	-0.3404144	-0.31705585	-0.292849274	-0.2405119713	-0.225564837	-0.161728863	0.272805242	1.9855277	2.4754762	2.5196483
AT1G66725	-0.8413585	-0.88074266	-0.672666618	-0.5263664934	-0.425877187	-0.173234296	0.950211683	1.8942547	2.0066724	1.6682482
AT2G31570	-1.2301238	-1.18916807	-0.818707686	-0.6774019433	-0.647406013	-0.557148160	-0.157165103	1.0827289	3.0171150	3.1806977
AT4G37010	-1.1426906	-1.04851771	-0.176408945	0.0106280991	0.223705129	0.357114240	1.338770039	3.3656682	5.5192869	5.4216938
AT5G15180	-1.0923623	-1.09343550	-0.883423969	-0.7943016232	-0.731386438	-0.573229362	0.683180875	4.4848112	7.8387598	9.1738217
AT5G44130	-0.5068211	-0.50476819	-0.460284177	-0.2870945664	-0.196276140	-0.007526504	0.685202305	2.5798811	4.5167909	5.2872617
AT5G53370	-0.5807505	-0.56375986	-0.074017693	0.4433051226	0.597628910	1.077355395	2.489457155	4.6555237	6.0946946	7.4280850
AT5G63600	-1.4076945	-1.34426199	-0.950976607	-0.8536102760	-0.825039383	-0.496535257	0.894586565	2.3451309	3.0851778	3.2928712
AT3G28940	-0.8267220	-0.86126587	-0.620511067	-0.5052665695	-0.411478638	-0.322854972	-0.052691485	0.8990039	2.2812660	2.5188865
AT4G09760	0.2979092	0.04599711	-0.219205538	-0.2687656356	-0.279981082	-0.205216469	0.273757302	2.0072180	3.4340299	3.8674314
AT3G48890	-0.4059805	-0.42858406	-0.267885962	-0.0004057439	-0.005243846	0.152895326	0.436089615	1.2495969	1.9688484	3.1484785
AT5G19230	-1.2179382	-1.28815167	-0.916802820	-0.8915080597	-0.855871406	-0.619930529	0.169518899	1.9100807	3.2136827	4.9477881
AT3G18170	-0.4271698	-0.50125792	-0.262497291	-0.2676837946	-0.265512860	-0.204452211	0.093003012	1.4245544	3.0627997	4.8484462
AT4G25900	-0.9813610	-0.95350247	-0.518273918	-0.1892881499	-0.169471213	-0.029966340	0.005774842	0.3150573	0.6743071	1.1637587
AT5G44380	-2.0117815	-1.97765704	-1.490264836	-1.1244348132	-0.942767099	-0.529873082	0.779433172	2.8213110	4.0644523	5.5585071
AT2G27920	-0.3505470	-0.36880057	-0.335433957	-0.2534726802	-0.282127762	-0.221513501	0.027456556	1.1422515	2.9809897	4.4176378
AT4G15610	-1.4550514	-1.50867435	-1.046588167	-0.8654570386	-0.753390915	-0.527470320	0.191034251	1.6526311	2.6269300	4.0501623
AT3G17420	-0.7956779	-0.86764825	-0.752382855	-0.5957468266	-0.587627111	-0.444276601	0.148891346	1.4217575	2.0685579	3.2194249
AT1G08930	-0.6456934	-0.71843189	0.007498428	0.3669447964	0.529562703	0.836792977	1.531232351	2.9947148	4.3550962	5.7712578

# annotation bar for consensus time

col_fun <- brewer.pal(10,"Spectral")

names(col_fun) <- c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9")
col_fun

ha = HeatmapAnnotation(`Consensus Time Group` = c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"), col = list(`Consensus Time Group` = col_fun), show_legend = F)

T0

'#9E0142'

T1

'#D53E4F'

T2

'#F46D43'

T3

'#FDAE61'

T4

'#FEE08B'

T5

'#E6F598'

T6

'#ABDDA4'

T7

'#66C2A5'

T8

'#3288BD'

T9

'#5E4FA2'

# quantile normalize

mat = cor_sub_m
mat2 = t(apply(mat, 1, function(x) {
    q10 = quantile(x, 0.1)
    q90 = quantile(x, 0.9)
    x[x < q10] = q10
    x[x > q90] = q90
    scale(x)
}))
colnames(mat2) = colnames(mat)

# side annotation to match cortex colors

cortex_sa = rowAnnotation(foo = anno_block(gp = gpar(fill = "#82B6FF"),
        labels = c("Cortex"), 
        labels_gp = gpar(col = "black", fontsize = 18)))

feature_names %>% filter(gene == "AT1G09750")

A tibble: 1 × 2

gene	Name
<chr>	<chr>
AT1G09750	CORTEX

# genes to label on side of heatmap for cortex

(cor_mat_for_mark <- data.frame(mat2) %>% 
 rownames_to_column("gene") %>%
 left_join(feature_names) %>% 
 mutate(index=dplyr::row_number()) %>%
select(Name, index) %>%
filter(Name %in% c("AT1G17285", "CYP75B1", "NPF6.4", "PER69", "AT4G09760")))

Joining, by = "gene"

A data.frame: 4 × 2

Name	index
<chr>	<int>
AT1G17285	21
CYP75B1	45
NPF6.4	65
AT4G09760	81

# at - index of genes you want to mark
# labels - Names to show

cor_mark_anno = rowAnnotation(foo = anno_mark(at = cor_mat_for_mark$index, labels = cor_mat_for_mark$Name, labels_gp = gpar(col = "black", fontsize = 16)))

# ['#d8b365','#f5f5f5','#5ab4ac']
options(repr.plot.width=10, repr.plot.height=8)
Cortex_hm <- Heatmap(mat2, 
        col = colorRamp2(c(-1.5, 0, 1.5), 
                         c('blue','white','red')), 
                show_column_names = F,
                cluster_columns = F,
        cluster_rows=F,
                show_row_names = F, 
        show_heatmap_legend = F, 
        left_annotation=cortex_sa, 
        right_annotation=cor_mark_anno)

draw(Cortex_hm, padding = unit(c(10, 10, 10, 10), "mm"), heatmap_legend_side = "top")

# text annotation for consensus time

ht = columnAnnotation(foo = anno_text(c("T0", "T1", "T2", "T3", "T4", "T5", "T6", "T7", "T8", "T9"), rot = 0, just = "center", gp = gpar(fontsize = 16)))

options(repr.plot.width=12, repr.plot.height=10)


ht_list = ht %v% ha %v% Endo_hm %v% Cortex_hm

draw(ht_list, ht_gap = unit(0.2, "cm"), padding = unit(c(2, 2, 5, 10), "mm"), heatmap_legend_side = "bottom")
# grab heatmap as a grob to arrange in cowplot
hm_final <- grid.grabExpr(draw(ht_list, ht_gap = unit(0.2, "cm"), padding = unit(c(2, 2, 5, 10), "mm"), heatmap_legend_side = "bottom"))## bottom, left, top and right

# output heatmap as pdf

pdf("./supp_data/WT_Ground_tissue_heatmap.pdf", width = 12, height = 10)
draw(ht_list, ht_gap = unit(0.2, "cm"), padding = unit(c(2, 2, 5, 10), "mm"), heatmap_legend_side = "bottom") ## bottom, left, top and right
dev.off()

png: 2

options(repr.plot.width=8, repr.plot.height=6)
DefaultAssay(rc.integrated) <- "integrated"


(SCR <- FeaturePlot(rc.integrated, features = "AT3G54220",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("SCR"))

(MYB36 <- FeaturePlot(rc.integrated, features = "AT5G57620",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = F, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("MYB36"))

(CASP <- FeaturePlot(rc.integrated, features = "AT2G36100",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("CASP1"))

options(repr.plot.width=8, repr.plot.height=6)
(Cor_1 <- FeaturePlot(rc.integrated, features = "AT1G17285",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("AT1G17285"))

options(repr.plot.width=8, repr.plot.height=6)
(Cor_2 <- FeaturePlot(rc.integrated, features = "AT5G64620",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("C/VIF2"))

options(repr.plot.width=8, repr.plot.height=6)
(Cor_3 <- FeaturePlot(rc.integrated, features = "AT4G09760",
    cols = c("grey", "red"), label=F, repel=F, pt.size = 0.3, order = T, min.cutoff = "q1", max.cutoff = "q99") + ggtitle("AT4G09760"))

plot_anno <- function(rc.integrated){
order <- c("Quiescent Center", "Ground Tissue","Columella", "Lateral Root Cap", "Atrichoblast", "Trichoblast", "Cortex", "Endodermis", "Metaphloem & Companion Cell","Protophloem", "Xylem", "Procambium","Xylem Pole Pericycle","Phloem Pole Pericycle", "Protoxylem", "Metaxylem", "Unknown")
palette <- c("#9400d3", "#DCD0FF","#5ab953", "#bfef45", "#008080", "#21B6A8", "#82b6ff", "#0000FF","#e6194b", "#dd77ec", "#9a6324", "#ffe119", "#ff9900", "#ffd4e3", "#9a6324", "#ddaa6f", "#EEEEEE")
rc.integrated$celltype.anno <- factor(rc.integrated$celltype.anno, levels = order[sort(match(unique(rc.integrated$celltype.anno),order))])
color <- palette[sort(match(unique(rc.integrated$celltype.anno),order))]
p1 <- DimPlot(rc.integrated, reduction = "umap", group.by = "celltype.anno", cols=color) + ggtitle("Cell Type") + theme(plot.title = element_text(hjust = 0.5))
p2 <- DimPlot(rc.integrated, reduction = "umap", group.by = "time.anno",order = c("Distal Columella","Proximal Columella","Distal Lateral Root Cap","Proximal Lateral Root Cap","Maturation","Elongation","Meristem"),cols = c("#e5f5e0", "#a1d99b", "#31a354", "#deebf7", "#3182bd", '#fee0d2','#de2d26')) + ggtitle("Developmental Stage") + theme(plot.title = element_text(hjust = 0.5))
p3 <- DimPlot(rc.integrated, reduction = "umap", group.by = "consensus.time.group", cols=brewer.pal(10,"Spectral")) + ggtitle("Consensus Time") + theme(plot.title = element_text(hjust = 0.5))
options(repr.plot.width=25, repr.plot.height=8)
gl <- lapply(list(p1, p2, p3), ggplotGrob)
gwidth <- do.call(unit.pmax, lapply(gl, "[[", "widths"))
gl <- lapply(gl, "[[<-", "widths", value = gwidth)
gridExtra::grid.arrange(grobs=gl, ncol=3)
}

options(repr.plot.width=28, repr.plot.height=6)
top_umaps <- plot_anno(rc.integrated)

options(repr.plot.width=24, repr.plot.height=18)

gene_umaps <- plot_grid(SCR, MYB36, CASP, Cor_1, Cor_2, Cor_3, ncol=3, align="hv")

gene_hm <- plot_grid(gene_umaps, hm_final, ncol=2, rel_widths = c(1.6, 1))

options(repr.plot.width=28, repr.plot.height=18)

plot_grid(top_umaps, gene_hm, ncol=1, rel_heights = c(1, 1.5))

ggsave("./supp_data/Endo_cortex_traj_combined.pdf",
  width = 28,
  height = 18)

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /fast/home/c/chsu/anaconda3/envs/seu3/lib/libopenblasp-r0.3.12.so

locale:
 [1] LC_CTYPE=en_US.utf-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.utf-8        LC_COLLATE=en_US.utf-8    
 [5] LC_MONETARY=en_US.utf-8    LC_MESSAGES=en_US.utf-8   
 [7] LC_PAPER=en_US.utf-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.utf-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggrepel_0.8.2        circlize_0.4.12      ComplexHeatmap_2.6.2
 [4] cowplot_1.1.0        future_1.20.1        RColorBrewer_1.1-2  
 [7] Seurat_3.1.5         forcats_0.5.0        stringr_1.4.0       
[10] dplyr_1.0.2          purrr_0.3.4          readr_1.4.0         
[13] tidyr_1.1.2          tibble_3.0.4         ggplot2_3.3.2       
[16] tidyverse_1.3.0     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15          colorspace_2.0-0    rjson_0.2.20       
  [4] ellipsis_0.3.1      ggridges_0.5.2      IRdisplay_0.7.0    
  [7] GlobalOptions_0.1.2 base64enc_0.1-3     fs_1.5.0           
 [10] clue_0.3-58         rstudioapi_0.13     farver_2.0.3       
 [13] leiden_0.3.5        listenv_0.8.0       fansi_0.4.1        
 [16] lubridate_1.7.9     xml2_1.3.2          codetools_0.2-18   
 [19] splines_4.0.3       IRkernel_1.1.1.9000 jsonlite_1.7.1     
 [22] Cairo_1.5-12.2      broom_0.7.2         ica_1.0-2          
 [25] cluster_2.1.0       dbplyr_2.0.0        png_0.1-7          
 [28] uwot_0.1.8          sctransform_0.3.1   compiler_4.0.3     
 [31] httr_1.4.2          backports_1.2.0     assertthat_0.2.1   
 [34] Matrix_1.2-18       lazyeval_0.2.2      cli_2.1.0          
 [37] htmltools_0.5.0     tools_4.0.3         rsvd_1.0.3         
 [40] igraph_1.2.6        gtable_0.3.0        glue_1.4.2         
 [43] RANN_2.6.1          reshape2_1.4.4      Rcpp_1.0.5         
 [46] cellranger_1.1.0    vctrs_0.3.4         ape_5.4-1          
 [49] nlme_3.1-150        lmtest_0.9-38       globals_0.13.1     
 [52] ps_1.4.0            rvest_0.3.6         lifecycle_0.2.0    
 [55] irlba_2.3.3         MASS_7.3-53         zoo_1.8-8          
 [58] scales_1.1.1        hms_0.5.3           parallel_4.0.3     
 [61] reticulate_1.18     pbapply_1.4-3       gridExtra_2.3      
 [64] stringi_1.5.3       S4Vectors_0.28.0    BiocGenerics_0.36.0
 [67] shape_1.4.5         repr_1.1.0          rlang_0.4.8        
 [70] pkgconfig_2.0.3     matrixStats_0.57.0  evaluate_0.14      
 [73] lattice_0.20-41     ROCR_1.0-11         labeling_0.4.2     
 [76] patchwork_1.1.0     htmlwidgets_1.5.2   tidyselect_1.1.0   
 [79] parallelly_1.21.0   RcppAnnoy_0.0.16    plyr_1.8.6         
 [82] magrittr_1.5        R6_2.5.0            IRanges_2.24.0     
 [85] generics_0.1.0      pbdZMQ_0.3-3.1      DBI_1.1.0          
 [88] pillar_1.4.6        haven_2.3.1         withr_2.3.0        
 [91] fitdistrplus_1.1-1  survival_3.2-7      future.apply_1.6.0 
 [94] tsne_0.1-3          modelr_0.1.8        crayon_1.3.4       
 [97] uuid_0.1-4          KernSmooth_2.23-18  plotly_4.9.2.1     
[100] GetoptLong_1.0.5    readxl_1.3.1        data.table_1.13.2  
[103] reprex_0.3.0        digest_0.6.27       stats4_4.0.3       
[106] munsell_0.5.0       viridisLite_0.3.0